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@inproceedings{Peng_2019_ICCV,
author = {Peng, Xingchao and Bai, Qinxun and Xia, Xide and Huang, Zijun and Saenko, Kate and Wang, Bo},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},

title = {{Moment Matching for Multi-Source Domain Adaptation}},
year = {2019}
}
@article{Gama.2013,
abstract = {Most streaming decision models evolve continuously over time, run in resource-aware environments, and detect and react to changes in the environment generating data. One important issue, not yet convincingly addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of prequential error with forgetting mechanisms to provide reliable error estimators. We prove that, in stationary data and for consistent learning algorithms, the holdout estimator, the prequential error and the prequential error estimated over a sliding window or using fading factors, all converge to the Bayes error. The use of prequential error with forgetting mechanisms reveals to be advantageous in assessing performance and in comparing stream learning algorithms. It is also worthwhile to use the proposed methods for hypothesis testing and for change detection. In a set of experiments in drift scenarios, we evaluate the ability of a standard change detection algorithm to detect change using three prequential error estimators. These experiments point out that the use of forgetting mechanisms (sliding windows or fading factors) are required for fast and efficient change detection. In comparison to sliding windows, fading factors are faster and memoryless, both important requirements for streaming applications. Overall, this paper is a contribution to a discussion on best practice for performance assessment when learning is a continuous process, and the decision models are dynamic and evolve over time. (PsycINFO Database Record (c) 2013 APA, all rights reserved) (journal abstract)},
author = {Gama, Jo{\~{a}}o and Sebasti{\~{a}}o, Raquel and Rodrigues, Pedro Pereira},


issn = {08856125},
journal = {Machine Learning},
keywords = {Concept drift,Data streams,Evaluation design,Prequential analysis},
number = {3},
pages = {317--346},
title = {{On evaluating stream learning algorithms}},

volume = {90},
year = {2013}
}
@inproceedings{Peng2018a,
abstract = {The success of machine learning methods on visual recognition tasks is highly dependent on access to large labeled datasets. However, real training images are expensive to collect and annotate for both computer vision and robotic applications. The synthetic images are easy to generate but model performance often drops significantly on data from a new deployment domain, a problem known as dataset shift, or dataset bias. Changes in the visual domain can include lighting, camera pose and background variation, as well as general changes in how the image data is collected. While this problem has been studied extensively in the domain adaptation literature, progress has been limited by the lack of large-scale challenge benchmarks.},
author = {Peng, Xingchao and Usman, Ben and Kaushik, Neela and Wang, Dequan and Hoffman, Judy and Saenko, Kate},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Peng et al.{\_}2018{\_}IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.pdf:pdf},
isbn = {978-1-5386-6100-0},
issn = {21607516},
mendeley-groups = {Transfer Learning/Datasets/VISDA},
month = {jun},
pages = {2134-2139},
publisher = {IEEE},
title = {{VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation}},
volume = {2018-June},
year = {2018}
}

@inproceedings{DBLP:conf/iclr/ArjovskyB17,
author = {Arjovsky, Mart$\backslash$'$\backslash$in and Bottou, L{\'{e}}on},
booktitle = {5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Arjovsky, Bottou{\_}2017{\_}5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conferen.pdf:pdf},
publisher = {OpenReview.net},
title = {{Towards Principled Methods for Training Generative Adversarial Networks}},
url = {https://openreview.net/forum?id=Hk4{\_}qw5xe},
year = {2017}
}

@incollection{NIPS2014_5423,
author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
booktitle = {Advances in Neural Information Processing Systems 27},
editor = {Ghahramani, Z and Welling, M and Cortes, C and Lawrence, N D and Weinberger, K Q},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Goodfellow et al.{\_}2014{\_}Advances in Neural Information Processing Systems 27.pdf:pdf},
pages = {2672--2680},
publisher = {Curran Associates, Inc.},
title = {{Generative Adversarial Nets}},
url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf},
year = {2014}
}


@inproceedings{Ward2013,
abstract = {Though recent advanced convolutional neural networks (CNNs) have been improving the image recognition ac- curacy, the models are getting more complex and time- consuming. For real-world applications in industrial and commercial scenarios, engineers and developers are often faced with the requirement of constrained time budget. In this paper, we investigate the accuracy ofCNNs under con- strained time cost. Under this constraint, the designs ofthe network architectures should exhibit as trade-offs among the factors like depth, numbers of filters, filter sizes, etc. With a series of controlled comparisons, we progressively modify a baseline model while preserving its time complex- ity. This is also helpful for understanding the importance of the factors in network designs. We present an architecture that achieves very competitive accuracy in the ImageNet dataset (11.8{\%} top-5 error, 10-view test), yet is 20{\%} faster than “AlexNet” [14] (16.0{\%} top-5 error, 10-view test).},
author = {He, Kaiming and Sun, Jian},
booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Ward, Johnson{\_}2013{\_}Plant Biology.pdf:pdf},
isbn = {978-1-4673-6964-0},
issn = {14358603},
keywords = {Asclepiadoideae,Floral rewards,Generalisation,Invasive plants,Pollination},
mendeley-groups = {Paper/ASN},
month = {jun},
pages = {5353--5360},
pmid = {23173573},
publisher = {IEEE},
title = {{Convolutional neural networks at constrained time cost}},
volume = {15},
year = {2015}
}
@inproceedings{Li2019c,
abstract = {Domain adaptation aims to transfer the enriched label knowledge from large amounts of source data to unlabeled target data. It has raised significant interest in multimedia analysis. Existing researches mainly focus on learning domain-wise transferable representations via statistical moment matching or adversarial adaptation techniques, while ignoring the class-wise mismatch across domains, resulting in inaccurate distribution alignment. To address this issue, we propose a Joint Adversarial Domain Adaptation (JADA) approach to simultaneously align domain-wise and class-wise distributions across source and target in a unified adversarial learning process. Specifically, JADA attempts to solve two complementary minimax problems jointly. The feature generator aims to not only fool the well-trained domain discriminator to learn domain-invariant features, but also minimize the disagreement between two distinct task-specific classifiers' predictions to synthesize target features near the support of source class-wisely. As a result, the learned transferable features will be equipped with more discriminative structures, and effectively avoid mode collapse. Additionally, JADA enables an efficient end-to-end training manner via a simple back-propagation scheme. Extensive experiments on several real-world cross-domain benchmarks, including VisDA-2017, ImageCLEF, Office-31 and digits, verify that JADA can gain remarkable improvements over other state-of-the-art deep domain adaptation approaches.},
address = {New York, NY, USA},
author = {Li, Shuang and Liu, Chi Harold and Xie, Binhui and Su, Limin and Ding, Zhengming and Huang, Gao},
booktitle = {Proceedings of the 27th ACM International Conference on Multimedia},
doi = {10.1145/3343031.3351070},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Li et al.{\_}2019{\_}Proceedings of the 27th ACM International Conference on Multimedia.pdf:pdf},
isbn = {9781450368896},
keywords = {Adversarial Learning,Back-propagation,Domain Adaptation,Gradient Reversal Layer,Representation Learning},
month = {oct},
pages = {729--737},
publisher = {ACM},
title = {{Joint Adversarial Domain Adaptation}},
url = {https://dl.acm.org/doi/10.1145/3343031.3351070},
year = {2019}
}

@article{Bro2007,
abstract = {Many modem data analysis methods involve computing a matrix singular value decomposition (SVD) or eigenvalue decomposition (EVD). Principal components analysis is the time-honored example, but more recent applications include latent semantic indexing, hypertext induced topic selection (HITS), clustering, classification, etc. Though the SVD and EVD are well-established and can be computed via state-of-the-art algorithms, it is not commonly mentioned that there is an intrinsic sign indeterminacy that can significantly impact the conclusions and interpretations drawn from their results. Here we provide a solution to the sign ambiguity problem and show how it leads to more sensible solutions.},
author = {Bro, R. and Acar, E. and Kolda, Tamara G.},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Bro, Acar, Kolda{\_}2007{\_}SANDIA Report.pdf:pdf},
issn = {08869383},
journal = {Journal of Chemometrics},
mendeley-groups = {Paper/ASN},
month = {feb},
number = {2},
pages = {135--140},
title = {{Resolving the sign ambiguity in the singular value decomposition}},
volume = {22},
year = {2008}
}


@article{Deng2000,
abstract = {An algorithm of evolving self-organizing map (ESOM) is$\backslash$nproposed as a dynamic version of the Kohonen$\backslash$nself-organizing map, where network structure is evolved in$\backslash$nan on-line adaptive mode. Experiments have been carried out$\backslash$non some benchmark data sets as well as on macroeconomic$\backslash$ndata. Results show that ESOM is a good tool for clustering,$\backslash$ndata analysis, and visualization.},
author = {{Da Deng} and Kasabov, N.},


isbn = {0-7695-0619-4},
issn = {1098-7576},
journal = {Proceedings of the International Joint Conference on Neural Networks},
pages = {3--8 vol.6},
title = {{ESOM: an algorithm to evolve self-organizing maps from online data streams}},
volume = {6},
year = {2002}
}
@article{Cortes1995a,
abstract = {Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r (2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.},
author = {Cortes, Corinna and Vapnik, Vladimir},


isbn = {0885-6125},
issn = {1747-0285},
journal = {Machine Learning},
keywords = {att,com,corinna,efficient learning algorithms,neural,neural networks,pattern recognition,polynomial classifiers,radial basis function classifiers},
number = {20},
pages = {273--297},
pmid = {19549084},
title = {{Supprot-Vector Networks}},

volume = {20},
year = {1995}
}
@inproceedings{Bousmalis2017,
author = {Bousmalis, Konstantinos and Silberman, Nathan and Dohan, David and Erhan, Dumitru and Krishnan, Dilip},
booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},


isbn = {978-1-5386-0457-1},
month = {jul},
pages = {95--104},
publisher = {IEEE},
title = {{Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks}},

year = {2017}
}
@book{varga2010,
abstract = {1. Basic theory -- 2. Gergos̆ian-type eigenvalue inclusion theorems -- 3. More eigenvalue incluson results -- 4. Minimal Gergos̆ian sets and their sharpness -- 5. G-functions -- 6. Gergos̆ian-type theorems for partitioned matrices -- Appendices: -- A. Gergos̆ian-type theorems for partitioned matrices -- B. Vector norms and induced operator norms -- C. The person-Frobenius theory of nonnegative matrices -- D. Matlab 6 programs.},
address = {Berlin, Heidelberg},
author = {Varga, Richard S.},

isbn = {978-3-540-21100-6},
publisher = {Springer Berlin Heidelberg},
series = {Springer Series in Computational Mathematics},
title = {{Ger{\v{s}}gorin and His Circles}},

volume = {36},
year = {2004}
}
@inproceedings{Aggarwal.2003,
author = {Aggarwal, Charu C and Han, Jiawei and Wang, Jianyong and Yu, Philip S},
booktitle = {{\{}VLDB{\}} 2003, Proceedings of 29th International Conference on Very Large Data Bases, September 9-12, 2003, Berlin, Germany},

pages = {81--92},
title = {{Aggarwal et al. - 2003 - A Framework for Clustering Evolving Data Streams - Proceedings of the 29th international conference.pdf}},

year = {2003}
}
@book{van1983matrix,
author = {{Van Loan}, Charles F and Golub, Gene H},
isbn = {978-1421407944},
publisher = {Johns Hopkins University Press},
title = {{Matrix computations}},
year = {1983}
}
@inproceedings{Cutkosky.2017,
abstract = {Most online optimization algorithms focus on one of two things: performing well in adversarial settings by adapting to unknown data parameters (such as Lipschitz constants), typically achieving O(√ T) regret, or performing well in stochastic settings where they can leverage some structure in the losses (such as strong convexity), typically achieving O(log(T)) regret. Algorithms that focus on the former problem hitherto achieved O(√ T) in the stochastic setting rather than O(log(T)). Here we introduce an online optimization algorithm that achieves O(log 4 (T)) regret in a wide class of stochastic settings while gracefully degrading to the optimal O(√ T) regret in adversarial settings (up to logarithmic factors). Our algorithm does not require any prior knowledge about the data or tuning of parameters to achieve superior performance.},
author = {Cutkosky, Ashok and Boahen, Kwabena A},
booktitle = {Advances in Neural Information Processing Systems 30},

issn = {10495258},
number = {Nips},
pages = {5059--5067},
title = {{Stochastic and Adversarial Online Learning without Hyperparameters}},

year = {2017}
}
@inproceedings{Bifet.2013,
abstract = {In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on past concept drifts while being able to adapt quickly to new ones. We exemplify PAW with lazy learning methods in two variations: one to handle concept drift explicitly, and the other to add classifier diversity using an ensemble. Along with the standard measures of accuracy and time and memory use, we compare classifiers against state-of-the-art classifiers from the data-stream literature.},
address = {New York, NY, USA},
author = {Bifet, Albert and Pfahringer, Bernhard and Read, Jesse and Holmes, Geoff},
booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing},


isbn = {978-1-4503-1656-9},
pages = {801},
publisher = {ACM},
series = {SAC '13},
title = {{Efficient data stream classification via probabilistic adaptive windows}},

year = {2013}
}
@article{Musco2017,
author = {Musco, Cameron and Musco, Christopher},

journal = {Advances in Neural Information Processing Systems},
number = {9},
title = {{Ridge leverage score sampling bounds}},
year = {2017}
}
@article{Long2014,
author = {Long, Mingsheng and Wang, Jianmin and Ding, Guiguang and Shen, Dou and Yang, Qiang},


issn = {1041-4347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
month = {jul},
number = {7},
pages = {1805--1818},
title = {{Transfer Learning with Graph Co-Regularization}},

volume = {26},
year = {2014}
}
@article{Song2018,
abstract = {A general solution to covariate shift is to minimize a distributional discrepancy between training and test data. To solve this problem, kernel-based learning methods minimize the discrepancy in a kernel-induced feature space by transforming training data to be similar to test data and learn the transformed data in the feature space. However, when they are applied to classification tasks, transformed training data points from different classes can be mixed up in the kernel-induced space since they ignore the class labels in training data. Therefore, we propose an adapted surrogate kernel that is able to manage the large class discrepancy. The proposed method incorporates the class discrepancy into the surrogate kernel which tries to minimize the discrepancy in a kernel-induced feature space. To do this, we interpret the surrogate kernel with the Nystr{\"{o}}m method which allows prior knowledge to be incorporated into kernel approximation. By using class discrepancy derived from training data as prior knowledge, the adapted surrogate kernel does not keep only the role of surrogate kernel, but also the large class discrepancy among classes. The experimental results on several classification tasks including text classification and WiFi localization prove that the proposed kernel results in significant improvement in classification performance under covariate shift.},
author = {Song, Hyun Je and Park, Seong Bae},


issn = {15684946},
journal = {Applied Soft Computing Journal},
keywords = {Adapted surrogate kernel,Covariate shift,Large class discrepancy,Nystr{\"{o}}m method},
pages = {435--442},
publisher = {Elsevier B.V.},
title = {{An adapted surrogate kernel for classification under covariate shift}},

volume = {69},
year = {2018}
}
@incollection{NIPS2006_3075,
author = {Huang, Jiayuan and Gretton, Arthur and Borgwardt, Karsten and Sch{\"{o}}lkopf, Bernhard and Smola, Alex J},
booktitle = {Advances in Neural Information Processing Systems 19},
editor = {Sch{\"{o}}lkopf, B and Platt, J C and Hoffman, T},

pages = {601--608},
publisher = {MIT Press},
title = {{Correcting Sample Selection Bias by Unlabeled Data}},

year = {2007}
}
@inproceedings{Biehl.19,
abstract = {We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments. Specifically, we study Learning Vector Quantization (LVQ) systems trained from a stream of high-dimensional, clustered data.We consider standard winner-takes-all updates known as LVQ1. Statistical properties of the input data change on the time scale defined by the training process. We apply analytical methods borrowed from statistical physics which have been used earlier for the exact description of learning in stationary environments. The suggested framework facilitates the computation of learning curves in the presence of virtual and real concept drift. Here we focus on timedependent class bias in the training data. First results demonstrate that, while basic LVQ algorithms are suitable for the training in non-stationary environments, weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes.},
author = {Biehl, Michael and Abadi, Fthi and G{\"{o}}pfert, Christina and Hammer, Barbara},
booktitle = {Advances in Intelligent Systems and Computing},


isbn = {9783030196417},
issn = {21945357},
keywords = {Concept drift,LVQ,Supervised learning,Weight decay},
pages = {210--221},
title = {{Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework}},

volume = {976},
year = {2020}
}
@inbook{Lopes2011,
abstract = {H 0 : P = P 0 , H 1 : P = P 0 . ⇒ We already know how to test this hypothesis using chi-squared goodness-of-fit test. If dis­ tribution P 0 is continuous we had to group the data and consider a weaker discretized null hypothesis. We will now consider a different test for H 0 based on a very different idea that avoids this discretization.},
address = {Berlin, Heidelberg},
author = {Lopes, Raul H. C.},
booktitle = {International Encyclopedia of Statistical Science},

editor = {Lovric, Miodrag},
isbn = {978-3-642-04898-2},
pages = {718--720},
publisher = {Springer Berlin Heidelberg},
title = {{Kolmogorov-Smirnov Test}},

year = {2011}
}
@inproceedings{bifet2006,
abstract = {We study the combination of Kalman filter and a recently proposed algorithm for dynamically maintaining a sliding window, for learning from streams of examples. We integrate this idea into two well-known learning algorithms, the Na{\"{i}}ve Bayes algorithm and the k-means clusterer. We show on synthetic data that the new algorithms do never worse, and in some cases much better, than the algorithms using only memoryless Kalman filters or sliding windows with no filtering.},
address = {Berlin, Heidelberg},
author = {Bifet, Albert and Gavald{\`{a}}, Ricard},
booktitle = {Discovery Science},

editor = {Todorovski, Ljup{\v{c}}o and Lavra{\v{c}}, Nada and Jantke, Klaus P},

isbn = {978-3-540-46493-8},
issn = {03029743},
pages = {29--40},
publisher = {Springer Berlin Heidelberg},
title = {{Kalman Filters and Adaptive Windows for Learning in Data Streams}},

year = {2006}
}
@inproceedings{pan2008transfer,
abstract = {The WiFi-based indoor localization problem (WILP) aims to detect the location of a client device given the signals received from various access points. WILP is a complex and very important task for many AI and ubiquitous computing applications. A major approach to solving this task is through machine learning, where up-to-date labeled training data are required in a large scale indoor environment. In this paper, we identify WILP as a transfer learning problem, because the WiFi data are highly dependent on contextual changes. We show that WILP can be modeled as a transfer learning problem for regression modeling, where we identify several important cases of knowledge transfer that range from transferring the localization models over time, across space and across client devices. We also share our working experience in WILP and transfer learning research in a realistic problem solving setting, and discuss a data set we have made public for advancing this research. Copyright {\textcopyright} 2008, Association for the Advancement of Artificial Intelligence.},
author = {Pan, Sinno Jialin and Zheng, Vincent Wenchen and Yang, Qiang and Hu, Derek Hao},
booktitle = {Association for the advancement of artificial intelligence (AAAI) workshop},

organization = {The Association for the Advancement of Artificial Intelligence Palo Alto},
pages = {6},
title = {{Transfer learning for wifi-based indoor localization}},
volume = {6},
year = {2008}
}
@incollection{NIPS2019_8949,
author = {Zhao, Sicheng and Li, Bo and Yue, Xiangyu and Gu, Yang and Xu, Pengfei and Hu, Runbo and Chai, Hua and Keutzer, Kurt},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {7285--7298},
publisher = {Curran Associates, Inc.},
title = {{Multi-source Domain Adaptation for Semantic Segmentation}},

year = {2019}
}
@article{Ippel2016,
abstract = {{\textcopyright} 2016 Hogrefe Publishing. Novel technological advances allow distributed and automatic measurement of human behavior. While these technologies provide exciting new research opportunities, they also provide challenges: datasets collected using new technologies grow increasingly large, and in many applications the collected data are continuously augmented. These data streams make the standard computation of well-known estimators inefficient as the computation has to be repeated each time a new data point enters. In this tutorial paper, we detail online learning, an analysis method that facilitates the efficient analysis of Big Data and continuous data streams. We illustrate how common analysis methods can be adapted for use with Big Data using an online, or "row-by-row," processing approach. We present several simple (and exact) examples of the online estimation and discuss Stochastic Gradient Descent as a general (approximate) approach to estimate more complex models. We end this article with a discussion of the methodological challenges that remain.},
author = {Ippel, Lianne and Kaptein, Maurits and Vermunt, Jeroen},


isbn = {0030385660},
issn = {16142241},
journal = {Methodology},
keywords = {Big Data,Data streams,Machine learning,Online learning,Stochastic Gradient Descent},
number = {4},
pages = {124--138},
title = {{Dealing with data streams: An online, row-by-row, estimation tutorial}},
volume = {12},
year = {2016}
}
@inproceedings{Loezer2020,
abstract = {The data imbalance problem hampers the classification task. In streaming environments, this becomes even more cumbersome as the proportion of classes can vary over time. Approaches based on misclassification costs can be used to mitigate this problem. In this paper, we present the Cost-sensitive Adaptive Random Forest (CSARF) and compare it to the Adaptive Random Forest (ARF) and ARF with Resampling (ARFRE) in six real-world and six synthetic data sets with different class ratios. The empirical study analyzes two misclassification costs strategies of the CSARF and shows that the CSARF obtained statistically superior w.r.t. the average recall and average F1 when compared to ARF.},
address = {New York, NY, USA},
author = {Loezer, Lucas and Enembreck, Fabr{\'{i}}cio and Barddal, Jean Paul and {de Souza Britto}, Alceu},
booktitle = {Proceedings of the 35th Annual ACM Symposium on Applied Computing},


isbn = {9781450368667},
keywords = {Adaptive random forest,Cost-sensitive,Data stream,Ensemble,Imbalanced datasets},
month = {mar},
pages = {498--504},
publisher = {ACM},
title = {{Cost-sensitive learning for imbalanced data streams}},

year = {2020}
}
@article{Yan2006,
abstract = {Dimensionality reduction is an essential data preprocessing technique for large-scale and streaming data classification tasks. It can be used to improve both the efficiency and the effectiveness of classifiers. Traditional dimensionality reduction approaches fall into two categories: Feature Extraction and Feature Selection. Techniques in the feature extraction category are typically more effective than those in feature selection category. However, they may break down when processing large-scale data sets or data streams due to their high computational complexities. Similarly, the solutions provided by the feature selection approaches are mostly solved by greedy strategies and, hence, are not ensured to be optimal according to optimized criteria. In this paper, we give an overview of the popularly used feature extraction and selection algorithms under a unified framework. Moreover, we propose two novel dimensionality reduction algorithms based on the Orthogonal Centroid algorithm (OC). The first is an Incremental OC (IOC) algorithm for feature extraction. The second algorithm is an Orthogonal Centroid Feature Selection (OCFS) method which can provide optimal solutions according to the OC criterion. Both are designed under the same optimization criterion. Experiments on Reuters Corpus Volume-1 data set and some public large-scale text data sets indicate that the two algorithms are favorable in terms of their effectiveness and efficiency when compared with other state-of-the-art algorithms. {\textcopyright} 2006 IEEE.},
author = {Yan, Jun and Zhang, Benyu and Liu, Ning and Yan, Shuicheng and Cheng, Qiansheng and Fan, Weiguo and Yang, Qiang and Xi, Wensi and Chen, Zheng},


isbn = {1041-4347 VO  - 18},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Feature extraction,Feature selection,Orthogonal centroid algorithm},
number = {3},
pages = {320--332},
title = {{Effective and efficient dimensionality reduction for large-scale and streaming data preprocessing}},

volume = {18},
year = {2006}
}
@book{Scholkopf.2001,
abstract = {The present thesis can take its place among the numerous doctoral theses and other publications that are currently revolutionizing the area of machine learning . The author{\&}apos;s basic concern is with kernel{\{}based methods and in particular Support Vector algorithms for regression ...{\}}},
address = {Cambridge, MA, USA},
author = {Williams, Christopher K. I},
booktitle = {Journal of the American Statistical Association},

isbn = {0262194759},
issn = {0162-1459},
number = {462},
pages = {489--489},
publisher = {MIT Press},
title = {{Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond}},

volume = {98},
year = {2003}
}
@article{Nova2014,
abstract = {In this work we present a review of the state of the art of Learning Vector Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.},
archivePrefix = {arXiv},
arxivId = {1509.07093},
author = {Nova, David and Est{\'{e}}vez, Pablo A.},

eprint = {1509.07093},

issn = {09410643},
journal = {Neural Computing and Applications},
keywords = {Learning vector quantization,Likelihood ratio maximization,Margin maximization,Neural networks,Supervised learning},
number = {3-4},
pages = {511--524},
title = {{A review of learning vector quantization classifiers}},
volume = {25},
year = {2014}
}
@inproceedings{Barddal.2015,
abstract = {? 2015 IEEE.Mining data streams is of the utmost importance due to its appearance in many real-world situations, such as: sensor networks, stock market analysis and computer networks intrusion detection systems. Data streams are, by definition, potentially unbounded sequences of data that arrive intermittently at rapid rates. Extracting useful knowledge from data streams embeds virtually all problems from conventional data mining with the addition of single-pass real-time processing within limited time and memory space. Additionally, due to its ephemeral nature, it is expected that streams undergo changes in its data distribution denominated concept drifts. In this work, we focus on one specific kind of concept drift that has not been extensively addressed in the literature, namely feature drift. A feature drift happens when changes occur in the set of features, such that a subset of features become, or cease to be, relevant to the learning problem. Specifically, changes in the relevance of features directly imply modifications in the decision boundary to be learned, thus the learner must detect and adapt to according to it. Timely detection and recover from feature drifts is a challenging task that can be modeled after a dynamic feature selection problem. In this paper we survey existing work on dynamic feature selection for data streams that acts either implicitly or explicitly. We conclude that there is a need for future research in this area, which we highlight as future research directions.},
author = {Barddal, Jean Paul and Gomes, Heitor Murilo and Enembreck, Fabr{\'{i}}cio},
booktitle = {Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI},


isbn = {9781509001637},
issn = {10823409},
pages = {1053--1060},
title = {{A survey on feature drift adaptation}},

volume = {2016-Janua},
year = {2016}
}
@inproceedings{DBLP:conf/iccv/HausserFMC17,
author = {H{\"{a}}usser, Philip and Frerix, Thomas and Mordvintsev, Alexander and Cremers, Daniel},
booktitle = {{\{}IEEE{\}} International Conference on Computer Vision, {\{}ICCV{\}} 2017, Venice, Italy, October 22-29, 2017},


pages = {2784--2792},
title = {{Associative Domain Adaptation}},

year = {2017}
}
@book{Sayed-mouchaweh,
abstract = {This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments.},
address = {Cham},
author = {Sayed-Mouchaweh, Moamar},


isbn = {978-3-319-25665-8},
publisher = {Springer International Publishing},
series = {SpringerBriefs in Applied Sciences and Technology},
title = {{Learning from Data Streams in Dynamic Environments}},

year = {2016}
}
@inproceedings{Marascu.2009,
abstract = {Outlyingness is a subjective concept relying on the isolation level of a (set of) record(s). Clustering-based outlier detection is a field that aims to cluster data and to detect outliers depending on their characteristics (small, tight and/or dense clusters might be considered as outliers). Existing methods require a parameter standing for the "level of outlyingness", such as the maximum size or a percentage of small clusters, in order to build the set of outliers. Unfortunately, manually setting this parameter in a streaming environment should not be possible, given the fast time response usually needed. In this paper we propose WOD, a method that separates outliers from clusters thanks to a natural and effective principle. The main advantages of WOD are its ability to automatically adjust to any clustering result and to be parameterless.},
author = {Marascu, Alice and Masseglia, Florent},
booktitle = {Proceedings of the 2009 {\{}ACM{\}} Symposium on Applied Computing (SAC), Honolulu, Hawaii, USA, March 9-12, 2009},


pages = {1491},
title = {{Parameterless outlier detection in data streams}},

year = {2009}
}
@article{Gomes2017,
abstract = {{\textcopyright} 2017, The Author(s). Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.},
author = {Gomes, Heitor M. and Bifet, Albert and Read, Jesse and Barddal, Jean Paul and Enembreck, Fabr{\'{i}}cio and Pfharinger, Bernhard and Holmes, Geoff and Abdessalem, Talel},


isbn = {0885-6125},
issn = {15730565},
journal = {Machine Learning},
keywords = {Concept drift,Data stream mining,Ensemble learning,Random forests},
number = {9-10},
pages = {1469--1495},
publisher = {Springer US},
title = {{Adaptive random forests for evolving data stream classification}},
volume = {106},
year = {2017}
}
@inproceedings{Wang2017a,
abstract = {Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation (W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods.},
annote = {1. MMD and Classwise MMD
2. Reweighting between marignal and conditional distribution},
archivePrefix = {arXiv},
arxivId = {1807.00516},
author = {Wang, Jindong and Chen, Yiqiang and Hao, Shuji and Feng, Wenjie and Shen, Zhiqi},
booktitle = {2017 IEEE International Conference on Data Mining (ICDM)},

eprint = {1807.00516},

isbn = {978-1-5386-3835-4},
issn = {15504786},
keywords = {Class imbalance,Distribution adaptation,Domain adaptation,Transfer learning},
month = {nov},
pages = {1129--1134},
publisher = {IEEE},
title = {{Balanced Distribution Adaptation for Transfer Learning}},

volume = {2017-Novem},
year = {2017}
}
@article{Liu2020,
abstract = {Machine learning relies on the availability of vast amounts of data for training. However, in reality, data are mostly scattered across different organizations and cannot be easily integrated due to many legal and practical constraints. To address this important challenge in the field of machine learning, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical modeling under a data federation. FTL allows knowledge to be shared without compromising user privacy and enables complementary knowledge to be transferred across domains in a data federation, thereby enabling a target-domain party to build flexible and effective models by leveraging rich labels from a source domain. This framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving transfer learning. It is flexible and can be effectively adapted to various secure multi-party machine learning tasks.},
author = {Liu, Yang and Kang, Yan and Xing, Chaoping and Chen, Tianjian and Yang, Qiang},


issn = {1541-1672},
journal = {IEEE Intelligent Systems},
keywords = {Data models,Encryption,Federated Learning,Homomorphic Encryption,Machine learning,Multi-party Computation,Neural networks,Organizations,Secret Sharing,Training,Transfer Learning},
number = {c},
pages = {1--1},
title = {{A Secure Federated Transfer Learning Framework}},

volume = {1672},
year = {2020}
}
@article{Koebel1981,
abstract = {The note gives a formula for a dynamic probability distribution. It is a non-homogeneous Markov chain. It developed from the problem of the diffusion of information. An illustrative example and results of a computer program are given.},
author = {Koebel, Michael},


issn = {00390631},
journal = {Statistische Hefte},
keywords = {diffusion of information,epidemic model,non-homogeneous Markov chain,probability distribution},
number = {1},
pages = {58--66},
title = {{A note on a dynamic probability distribution}},

volume = {22},
year = {1981}
}
@inproceedings{Cohen:2016:GMN:2897518.2897647,
abstract = {In this paper we provide faster algorithms for solving the geometric median problem: given {\$}n{\$} points in {\$}\backslashmathbb{\{}R{\}}{\^{}}{\{}d{\}}{\$} compute a point that minimizes the sum of Euclidean distances to the points. This is one of the oldest non-trivial problems in computational geometry yet despite an abundance of research the previous fastest algorithms for computing a {\$}(1+\backslashepsilon){\$}-approximate geometric median were {\$}O(d\backslashcdot n{\^{}}{\{}4/3{\}}\backslashepsilon{\^{}}{\{}-8/3{\}}){\$} by Chin et. al, {\$}\backslashtilde{\{}O{\}}(d\backslashexp{\{}\backslashepsilon{\^{}}{\{}-4{\}}\backslashlog\backslashepsilon{\^{}}{\{}-1{\}}{\}}){\$} by Badoiu et. al, {\$}O(nd+\backslashmathrm{\{}poly{\}}(d,\backslashepsilon{\^{}}{\{}-1{\}}){\$} by Feldman and Langberg, and {\$}O((nd){\^{}}{\{}O(1){\}}\backslashlog\backslashfrac{\{}1{\}}{\{}\backslashepsilon{\}}){\$} by Parrilo and Sturmfels and Xue and Ye. In this paper we show how to compute a {\$}(1+\backslashepsilon){\$}-approximate geometric median in time {\$}O(nd\backslashlog{\^{}}{\{}3{\}}\backslashfrac{\{}1{\}}{\{}\backslashepsilon{\}}){\$} and {\$}O(d\backslashepsilon{\^{}}{\{}-2{\}}){\$}. While our {\$}O(d\backslashepsilon{\^{}}{\{}-2{\}}){\$} is a fairly straightforward application of stochastic subgradient descent, our {\$}O(nd\backslashlog{\^{}}{\{}3{\}}\backslashfrac{\{}1{\}}{\{}\backslashepsilon{\}}){\$} time algorithm is a novel long step interior point method. To achieve this running time we start with a simple {\$}O((nd){\^{}}{\{}O(1){\}}\backslashlog\backslashfrac{\{}1{\}}{\{}\backslashepsilon{\}}){\$} time interior point method and show how to improve it, ultimately building an algorithm that is quite non-standard from the perspective of interior point literature. Our result is one of very few cases we are aware of outperforming traditional interior point theory and the only we are aware of using interior point methods to obtain a nearly linear time algorithm for a canonical optimization problem that traditionally requires superlinear time. We hope our work leads to further improvements in this line of research.},
address = {New York, NY, USA},
author = {Cohen, Michael B. and Lee, Yin Tat and Miller, Gary and Pachocki, Jakub and Sidford, Aaron},
booktitle = {Proceedings of the Forty-eighth Annual ACM Symposium on Theory of Computing},


isbn = {978-1-4503-4132-5},
keywords = {geometric median,interior point methods,stochastic gradient descent},
pages = {9--21},
publisher = {ACM},
series = {STOC '16},
title = {{Geometric median in nearly linear time}},

year = {2016}
}
@inproceedings{pmlr-v9-david10a,
abstract = {The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) Similarity between the unlabeled distributions, (ii) Existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) The covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient toguarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions. We also discuss the intuitively appealing paradigm of reweighing the labeled training sample according to the target unlabeled distribution. We show that, somewhat counter intuitively, that paradigm cannot be trusted in the following sense. There are DA tasks that are indistinguishable, as far as the input training data goes, but in which reweighing leads to significant improvement in one task, while causing dramatic deterioration of the learning success in the other.},
address = {Chia Laguna Resort, Sardinia, Italy},
author = {David, Shai Ben and Lu, Tyler and Luu, Teresa and Pal, David},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
editor = {Teh, Yee Whye and Titterington, Mike},

pages = {129--136},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Impossibility Theorems for Domain Adaptation}},

volume = {9},
year = {2010}
}
@inbook{Minku2019,
abstract = {Objectives: To examine the association between low positive affect, somatic anxiety and general distress with affective disorders, anxious misery, and visceral fear among coronary heart disease patients. Participants: Patients awaiting a coronary revascularization procedure (N = 158; 20.9{\%} female; median age = 65, interquartile range 58-73) underwent structured interview with the Mini-International Neuropsychiatric Interview. Patients completed a brief version of the Mood and Anxiety Symptom Questionnaire (i.e., Anxiety Depression Distress Inventory-27) and a measure of Type D personality. Results: Somatic anxiety scores yielded an area under the curve (AUC) = .784 and 75.0{\%} sensitivity and 68.5{\%} specificity in relation to panic disorder. Low positive affect yielded AUC = .811 and 70.4{\%} sensitivity and 77.1{\%} specificity for major depression. General distress yielded AUC = .795 and 75.0{\%} sensitivity and 72.5{\%} specificity for generalized anxiety disorder. No affective dimension was optimally associated with the anxious misery or visceral fear cluster. Trait negative affect was not a suitable screener for any disorder. Conclusions: The Anxiety Depression Distress Inventory-27 dimensions of low positive affect and somatic anxiety provided optimal detection of depression and panic disorder, respectively, as hypothesized, supporting discriminant validity. {\textcopyright} 2012 Wiley Periodicals, Inc.},
address = {Cham},
author = {Minku, Leandro L.},
booktitle = {Learning from Data Streams in Evolving Environments: Methods and Applications},

editor = {Sayed-Mouchaweh, Moamar},

isbn = {978-3-319-89803-2},
pages = {13--37},
publisher = {Springer International Publishing},
title = {{Transfer Learning in Non-stationary Environments}},

year = {2018}
}
@book{10.5555/248979,
address = {USA},
author = {Golub, Gene H and {Van Loan}, Charles F},
isbn = {0801854148},
publisher = {Johns Hopkins University Press},
title = {{Matrix Computations (3rd Ed.)}},
year = {1996}
}
@inproceedings{8576059,
author = {Qian, Chen and Lu, Hengyang and Wang, Chongjun},
booktitle = {Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI},


isbn = {9781538674499},
issn = {10823409},
keywords = {Homogeneous transfer,Online learning,Online transfer learning,Transfer learning},
month = {nov},
pages = {344--349},
title = {{Online single homogeneous source transfer learning based on adaboost}},
volume = {2018-Novem},
year = {2018}
}
@article{Li2016,
author = {Li, Xiao and Fang, Min and Zhang, Ju-Jie and Wu, Jinqiao},


issn = {09235965},
journal = {Signal Processing: Image Communication},
month = {may},
pages = {92--100},
title = {{Sample selection for visual domain adaptation via sparse coding}},

volume = {44},
year = {2016}
}
@article{DBLP:journals/jmlr/GaninUAGLLML16,
annote = {DANN},
author = {Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, Fran{\c{c}}ois and Marchand, Mario and Lempitsky, Victor S},

journal = {J. Mach. Learn. Res.},
pages = {59:1----59:35},
title = {{Domain-Adversarial Training of Neural Networks}},

volume = {17},
year = {2016}
}
@article{Glorot2010,
abstract = {We study a family of "classical" orthogonal polynomials which satisfy (apart from a 3-term recurrence relation) an eigenvalue problem with a differential operator of Dunkl-type. These polynomials can be obtained from the little {\$}q{\$}-Jacobi polynomials in the limit {\$}q=-1{\$}. We also show that these polynomials provide a nontrivial realization of the Askey-Wilson algebra for {\$}q=-1{\$}.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Vinet, Luc and Zhedanov, Alexei},

eprint = {arXiv:1011.1669v3},

isbn = {9781937284275},
issn = {17518113},
journal = {Journal of Physics A: Mathematical and Theoretical},
number = {8},
pages = {249--256},
pmid = {25246403},
title = {{A 'missing' family of classical orthogonal polynomials}},

volume = {44},
year = {2011}
}
@article{Lai2015,
abstract = {{\textcopyright} Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaa1.org). All rights reserved. Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts. We conduct experiments on four commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.},
author = {Lai, Siwei and Xu, Liheng and Liu, Kang and Zhao, Jun},

isbn = {9781577357018},
journal = {Proceedings of the National Conference on Artificial Intelligence},
keywords = {NLP and Machine Learning Track},
pages = {2267--2273},
title = {{Recurrent convolutional neural networks for text classification}},
volume = {3},
year = {2015}
}
@article{Alpaydin1999,
abstract = {Dietterich (1998) reviews five statistical tests and proposes the 5 x 2 cv t test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5 x 2 cv t test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5 x 2 cv F test, that combines multiple statistics to get a more robust test. Simulation results show that this combined version of the test has lower type I error and higher power than 5 x 2 cv proper.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Alpaydm, Ethem},

eprint = {arXiv:1011.1669v3},

isbn = {3258059322},
issn = {0899-7667},
journal = {Neural Computation},
keywords = {Algorithms,Classification,Learning,Neural Networks (Computer),Statistics as Topic},
number = {8},
pages = {1885--1892},
pmid = {10578036},
title = {{Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms}},

volume = {11},
year = {2002}
}
@article{Huang2017,
abstract = {Second-generation feedstock, especially nonfood lignocellulosic biomass is a potential source for biofuel production. Cost-intensive physical, chemical, biological pretreatment operations and slow enzymatic hydrolysis make the overall process of lignocellulosic conversion into biofuels less economical than available fossil fuels. Lignocellulose conversions carried out at ≤50 °C have several limitations. Therefore, this review focuses on the importance of thermophilic bacteria and thermostable enzymes to overcome the limitations of existing lignocellulosic biomass conversion processes. The influence of high temperatures on various existing lignocellulose conversion processes and those that are under development, including separate hydrolysis and fermentation, simultaneous saccharification and fermentation, and extremophilic consolidated bioprocess are also discussed. {\textcopyright} 2012 Elsevier Ltd.},
archivePrefix = {arXiv},
arxivId = {1702.02284},
author = {Bhalla, Aditya and Bansal, Namita and Kumar, Sudhir and Bischoff, Kenneth M. and Sani, Rajesh K.},

eprint = {1702.02284},

isbn = {0008-4093},
issn = {18732976},
journal = {Bioresource Technology},
keywords = {Bioconversion,Biofuels,Cellulases,Lignocellulose,Thermophilic bacteria,Xylanases},
pages = {751--759},
pmid = {23246299},
title = {{Improved lignocellulose conversion to biofuels with thermophilic bacteria and thermostable enzymes}},

volume = {128},
year = {2013}
}
@article{wu2019domain,
author = {Wu, Yifan and Winston, Ezra and Kaushik, Divyansh and Lipton, Zachary},

journal = {arXiv preprint arXiv:1903.01689},
title = {{Domain adaptation with asymmetrically-relaxed distribution alignment}},
year = {2019}
}
@book{Adams2010,
abstract = {Product Description Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Br{\"{u}}ckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Takafumi Kanamori, Klaus-Robert M{\"{u}}ller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Sch{\"{o}}lkopf, Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama, Choon Hui Teo Neural Information Processing series},
author = {Adams, Niall},
booktitle = {Journal of the Royal Statistical Society: Series A (Statistics in Society)},


isbn = {9780262170055},
issn = {1467-985X},
keywords = {active learning,artificial intelligence,instance based learning,learning classifier system,machine learning,mathematics,multi task learning,semi supervised learning,unsupervised learning},
number = {1},
pages = {274--274},
pmid = {15288565},
title = {{Dataset Shift in Machine Learning: Book Reviews}},

volume = {173},
year = {2010}
}
@article{Villmann2017a,
abstract = {{\textcopyright} 2017 IEEE. The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust behavior nowadays powerful alternatives have increasing attraction. Particularly, deep architectures of multilayer networks achieve frequently very high accuracies and are, thanks to modern graphic processor units use for calculation, trainable in acceptable time. In this conceptual paper we show, how we can combine both network architectures to benefit from their advantages. For this purpose, we consider learning vector quantizers in terms of feedforward network architectures and explain how it can be combined effectively with multilayer or single-layer feedforward network architectures. This approach includes deep and flat architectures as well as the popular extreme learning machines. For the resulting networks, the multi-/ single-layer networks act as adaptive filters like in signal processing while the interpretability of the prototype-based learning vector quantizers is kept for the resulting filtered feature space. In this way a powerful combination of two successful architectures is obtained.},
author = {Villmann, T. and Biehl, M. and Villmann, A. and Saralajew, S.},


isbn = {9781509066384},
journal = {12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings},
title = {{Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning}},
year = {2017}
}
@article{Gonen2014,
abstract = {Transfer learning considers related but distinct tasks defined on heterogenous domains and tries to transfer knowledge between these tasks to improve generaliza-tion performance. It is particularly useful when we do not have sufficient amount of labeled training data in some tasks, which may be very costly, laborious, or even infeasible to obtain. Instead, learning the tasks jointly enables us to effectively increase the amount of labeled training data. In this paper, we formulate a ker-nelized Bayesian transfer learning framework that is a principled combination of kernel-based dimensionality reduction models with task-specific projection matrices to find a shared subspace and a coupled classification model for all of the tasks in this subspace. Our two main contributions are: (i) two novel probabilistic mod-els for binary and multiclass classification, and (ii) very efficient variational approximation procedures for these models. We illustrate the generalization performance of our algorithms on two different applications. In com-puter vision experiments, our method outperforms the state-of-the-art algorithms on nine out of 12 benchmark supervised domain adaptation experiments defined on two object recognition data sets. In cancer biology ex-periments, we use our algorithm to predict mutation status of important cancer genes from gene expression profiles using two distinct cancer populations, namely, patient-derived primary tumor data and in-vitro-derived cancer cell line data. We show that we can increase our generalization performance on primary tumors us-ing cell lines as an auxiliary data source.},
author = {G{\"{o}}nen, Mehmet and Margolin, Adam A},

isbn = {9781577356790},
journal = {Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence},
keywords = {Novel Machine Learning Algorithms},
number = {2011},
pages = {1831--1839},
title = {{Kernelized Bayesian Transfer Learning}},

year = {2014}
}
@article{Sato1995,
author = {Sato, A and Yamada, K},

journal = {Advances in Neural Information Processing Systems, NIPS},
number = {8},
pages = {423--429},
title = {{Generalized Learning Vector Quantization}},
year = {1995}
}
@inproceedings{Di2019,
address = {New York, New York, USA},
author = {Di, Shimin and Shen, Yanyan and Chen, Lei},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery {\&} Data Mining - KDD '19},


isbn = {9781450362016},
keywords = {2019,Transfer learning,acm reference format,and lei chen,relation extraction,relation extraction via,shimin di,transfer learning,yanyan shen},
pages = {1348--1357},
publisher = {ACM Press},
title = {{Relation Extraction via Domain-aware Transfer Learning}},

year = {2019}
}
@article{Chen2018,
author = {Chen, Zhong and Fang, Zhide and Zhao, Jiabin and Fan, Wei and Edwards, Andrea and Zhang, Kun},


isbn = {9781538650356},
journal = {Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018},
keywords = {Bandwidth selection,Local sampling,Online density estimation,Streaming data,Weighting factor optimization},
pages = {201--210},
publisher = {IEEE},
title = {{Online Density Estimation over Streaming Data: A Local Adaptive Solution}},
year = {2019}
}
@inproceedings{Gunnemann2013,
abstract = {Clustering graphs annotated with feature vectors has recently gained much attention. The goal is to detect groups of vertices that are densely connected in the graph as well as similar with respect to their feature values. While early approaches treated all dimensions of the feature space as equally important, more advanced techniques consider the varying relevance of dimensions for different groups. In this work, we propose a novel clustering method for graphs with feature vectors based on the principle of spectral clustering. Following the idea of subspace clustering, our method detects for each cluster an individual set of relevant features. Since spectral clustering is based on the eigendecomposition of the affinity matrix, which strongly depends on the choice of features, our method simultaneously learns the grouping of vertices and the affinity matrix. To tackle the fundamental challenge of comparing the clustering structures for different feature subsets, we define an objective function that is unbiased regarding the number of relevant features. We develop the algorithm SSCG and we show its application for multiple real-world datasets. {\textcopyright} 2013 IEEE.},
author = {Gunnemann, Stephan and Farber, Ines and Raubach, Sebastian and Seidl, Thomas},
booktitle = {2013 IEEE 13th International Conference on Data Mining},


isbn = {978-0-7695-5108-1},
issn = {15504786},
keywords = {attributed graphs,graphs,networks,spectral clustering,subspace clustering},
month = {dec},
pages = {231--240},
publisher = {IEEE},
title = {{Spectral Subspace Clustering for Graphs with Feature Vectors}},

year = {2013}
}
@article{Nishida2007,
abstract = {Detecting concept drift is important for dealing with real-world online learning problems. To detect concept drift in a small number of examples, methods that have an online classifier and monitor its prediction errors during the learning have been developed. We have developed such a detection method that uses a statistical test of equal proportions. Experimental results showed that our method performed well in detecting the concept drift in five synthetic datasets that contained various types of concept drift. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
author = {Nishida, Kyosuke and Yamauchi, Koichiro},


isbn = {3-540-75487-3, 978-3-540-75487-9},
journal = {Discovery Science},
pages = {264--269},
title = {{Detecting Concept Drift Using Statistical Testing}},

year = {2007}
}
@article{DBLP:journals/neco/SchleifT15,
abstract = {{\textcopyright} 2015 Massachusetts Institute of Technology. Efficient learning of a data analysis task strongly depends on the data representation. Most methods rely on (symmetric) similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel or branch-and-bound approaches. Similarities and dissimilarities are, however, often naturally obtained by nonmetric proximity measures that cannot easily be handled by classical learning algorithms. Major efforts have been undertaken to provide approaches that can either directly be used for such data or to make standard methods available for these types of data. We provide a comprehensive survey for the field of learning with nonmetric proximities. First, we introduce the formalism used in nonmetric spaces and motivate specific treatments for nonmetric proximity data. Second, we provide a systematization of the various approaches. For each category of approaches, we provide a comparative discussion of the individual algorithms and address complexity issues and generalization properties. In a summarizing section, we provide a larger experimental study for the majority of the algorithms on standard data sets. We also address the problem of large-scale proximity learning, which is often overlooked in this context and of major importance to make the method relevant in practice. The algorithms we discuss are in general applicable for proximity-based clustering, one-class classification, classification, regression, and embedding approaches. In the experimental part, we focus on classification tasks.},
author = {Schleif, Frank Michael and Tino, Peter},


issn = {1530888X},
journal = {Neural Computation},
number = {10},
pages = {2039--2096},
title = {{Indefinite proximity learning: A review}},

volume = {27},
year = {2015}
}
@inproceedings{stvm,
abstract = {Transfer learning supports classification in domains varying from the learning domain. Prominent applications can be found in Wifi-localization, sentiment classification or robotics. A recent study shows that approximation of training trough test environments is leading to proper performance and out-dates the strategy most transfer learning approaches pursue. Additionally, sparse transfer learning models are required to address technical limitations and the demand for interpretability due to recent privacy regulations. In this work, we propose a new transfer learning approach which approximates the learning environment, combine it with the sparse and interpretable probabilistic classification vector machine and compare our solution with standard benchmarks in the field.},
author = {Raab, Christoph and Schleif, Frank-Michael},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

editor = {Trollmann, Frank and Turhan, Anni-Yasmin},

isbn = {9783030001100},
issn = {16113349},
keywords = {Basis-Transfer,Probabilistic classification vector machine,Single Value Decomposition,Sparse classification,Transfer learning},
pages = {169--181},
publisher = {Springer International Publishing},
title = {{Sparse Transfer Classification for Text Documents}},

volume = {11117 LNAI},
year = {2018}
}
@article{Gama2009,
abstract = {Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that continuously evolve over time, run in resource-aware environments, detect and react to changes in the environ- ment generating data. One important issue, not yet con- veniently addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. There are no golden standards for assessing performance in non-stationary environments. This paper proposes a gen- eral framework for assessing predictive stream learning algo- rithms. We defend the use of Predictive Sequential methods for error estimate – the prequential error. The prequential error allows us to monitor the evolution of the performance of models that evolve over time. Nevertheless, it is known to be a pessimistic estimator in comparison to holdout es- timates. To obtain more reliable estimators we need some forgetting mechanism. Two viable alternatives are: sliding windows and fading factors. We observe that the prequen- tial error converges to an holdout estimator when estimated over a sliding window or using fading factors. We present illustrative examples of the use of prequential error estima- tors, using fading factors, for the tasks of: i) assessing per- formance of a learning algorithm; ii) comparing learning algorithms; iii) hypothesis testing using McNemar test; and iv) change detection using Page-Hinkley test. In these tasks, the prequential error estimated using fading factors provide reliable estimators. In comparison to sliding windows, fad- ing factors are faster and memory-less, a requirement for streaming applications. This paper is a contribution to a discussion in the good-practices on performance assessment when learning dynamic models that evolve over time.},
author = {Gama, Jo{\~{a}}o and Sebasti{\~{a}}o, Raquel and Rodrigues, Pedro Pereira},


isbn = {9781605584959},
issn = {00359238},
journal = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09},
keywords = {concept drift,data streams,evaluation design},
pages = {329},
title = {{Issues in evaluation of stream learning algorithms}},

year = {2009}
}
@incollection{Cline2013,
abstract = {Definitions: Given a complex matrix A having m rows and n columns, if $\sigma$ is a nonnegative scalar, and u and v are nonzero m-and n-vectors, respectively, such that Av = $\sigma$ u and A * u = $\sigma$ v, then $\sigma$ is a singular value of A and u and v are corresponding left and right singular vectors, respectively. (For generality it is assumed that the matrices here are complex, although given these results, the analogs for real matrices are obvious.) If, for a given positive singular value, there are exactly t linearly independent corresponding right singular vectors and t linearly independent corresponding left singular vectors, the singular value has multiplicity t and the space spanned by the right (left) singular vectors is the corresponding right (left) singular space. Given a complex matrix A having m rows and n columns, the matrix product U V * is a singular value decomposition for a given matrix A if r U and V , respectively, have orthonormal columns. r has nonnegative elements on its principal diagonal and zeros elsewhere. r A = U V},
author = {Cline, Alan and Dhillon, Inderjit},


month = {dec},
pages = {1027--1039},
title = {{Computation of the Singular Value Decomposition}},

year = {2013}
}
@article{Vijay2018,
author = {Vijay, Yash and Sengupta, Anurag and George, Koshy},


isbn = {9781538692769},
journal = {Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018},
keywords = {Latent Semantic Analysis,Text categorisation,word2vec},
pages = {650--657},
publisher = {IEEE},
title = {{A Statistical Evaluation of Vector-space Models for Text Categorisation}},
year = {2019}
}
@inproceedings{DBLP:journals/corr/abs-1301-3781,
author = {Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
booktitle = {1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings},

title = {{Efficient Estimation ofWord Representations in Vector Space}},

year = {2013}
}
@article{CANO2019104909,
abstract = {This paper proposes a model for estimating probabilities in the presence of abrupt concept drift. This proposal is based on a dynamic Bayesian network. As the exact estimation of the parameters is unfeasible we propose an approximate procedure based on discretizing both the possible probability values and the parameter representing the probability of change. The result is a method which is quite efficient in time and space (with a complexity directly related to the number of points used in the discretization) and providing very accurate predictions as well. These benefits are checked with a detailed comparison with other standard procedures based on variable size windows or forgetting rates.},
author = {Cano, Andr{\'{e}}s and G{\'{o}}mez-Olmedo, Manuel and Moral, Seraf{\'{i}}n},


issn = {0950-7051},
journal = {Knowledge-Based Systems},
keywords = {Change detection,Concept drift,Dynamic Bayesian networks,Propagation algorithms},
pages = {104909},
title = {{A Bayesian approach to abrupt concept drift}},

year = {2019}
}
@article{Lin2018,
author = {Lin, Jianzhe and Ward, Rabab and Wang, Z. Jane},


isbn = {9781538660706},
journal = {2018 IEEE 20th International Workshop on Multimedia Signal Processing, MMSP 2018},
keywords = {Hyperspectral image,Transfer learning,salient samples,supervised classification},
pages = {1--5},
publisher = {IEEE},
title = {{Deep transfer learning for hyperspectral image classification}},
year = {2018}
}
@article{Salperwyck2015,
abstract = {We present an on-line method for concept change detection on labeled data streams. Our detection method uses a bivariate supervised criterion to determine if the data in two windows come from the same distribution. Our method has no assumption neither on data distribution nor on change type. It has the ability to detect changes of different kinds (mean, variance...). Experiments show that our method performs better than wellknown methods from the literature. Additionally, except from the window sizes, no user parameter is required in our method.},
author = {Salperwyck, Christophe and Boull{\'{e}}, Marc and Lemaire, Vincent},


isbn = {9781479919604},
journal = {Proceedings of the International Joint Conference on Neural Networks},
keywords = {Integrated circuits,Nickel,Robustness},
title = {{Concept drift detection using supervised bivariate grids}},
volume = {2015-Septe},
year = {2015}
}
@inproceedings{pmlr-v97-zhang19i,
abstract = {This paper addresses the problem of unsupervised domain adaption from theoretical and algorithmic perspectives. Existing domain adaptation theories naturally imply minimax optimization algorithms, which connect well with the domain adaptation methods based on adversarial learning. However, several disconnections still exist and form the gap between theory and algorithm. We extend previous theories (Mansour et al., 2009c; Ben-David et al., 2010) to multiclass classification in domain adaptation, where classifiers based on the scoring functions and margin loss are standard choices in algorithm design. We introduce Margin Disparity Discrepancy, a novel measurement with rigorous generalization bounds, tailored to the distribution comparison with the asymmetric margin loss, and to the minimax optimization for easier training. Our theory can be seamlessly transformed into an adversarial learning algorithm for domain adaptation, successfully bridging the gap between theory and algorithm. A series of empirical studies show that our algorithm achieves the state of the art accuracies on challenging domain adaptation tasks.},
address = {Long Beach, California, USA},
author = {Zhang, Yuchen and Liu, Tianle and Long, Mingsheng and Jordan, Michael},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},

pages = {7404--7413},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Bridging Theory and Algorithm for Domain Adaptation}},

volume = {97},
year = {2019}
}
@inproceedings{Papadopoulo200,
abstract = {The Singular Value Decomposition (SVD) of a matrix is a linear algebra tool that has been successfully applied to a wide variety of domains. The present paper is concerned with the problem of estimating the Jacobian of the SVD components of a matrix with respect to the matrix itself. An exact analytic technique is developed that facilitates the estimation of the Jacobian using calculations based on simple linear algebra. Knowledge of the Jacobian of the SVD is very useful in certain applications involving multivariate regression or the computation of the uncertainty related to estimates obtained through the SVD. The usefulness and generality of the proposed technique is demonstrated by applying it to the estimation of the uncertainty for three different vision problems, namely self-calibration, epipole computation and rigid motion estimation.},
address = {Berlin, Heidelberg},
author = {Papadopoulo, Th{\'{e}}odore and Lourakis, Manolis I A},
booktitle = {Computer Vision - ECCV 2000},

isbn = {978-3-540-45054-2},
pages = {554--570},
publisher = {Springer Berlin Heidelberg},
title = {{Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications}},
year = {2000}
}

@inproceedings{AAAI1817155,
  author    = {Jian Shen and
               Yanru Qu and
               Weinan Zhang and
               Yong Yu},
  editor    = {Sheila A. McIlraith and
               Kilian Q. Weinberger},
  title     = {Wasserstein Distance Guided Representation Learning for Domain Adaptation},
  booktitle = {Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence,
               (AAAI-18), New Orleans, Louisiana, USA, February
               2-7, 2018},
  pages     = {4058--4065},
  publisher = {{AAAI} Press},
  year      = {2018},
  url       = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17155},
  timestamp = {Sun, 21 Apr 2019 10:04:41 +0200},
  biburl    = {https://dblp.org/rec/conf/aaai/ShenQZY18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{5288526,
abstract = {A survey on transfer learning. IEEE Trans. Knowl. Data Eng},
author = {Pan, Sinno Jialin and Yang, Qiang},


issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Transfer learning,data mining.,machine learning,survey},
number = {10},
pages = {1345--1359},
title = {{A survey on transfer learning}},
volume = {22},
year = {2010}
}
@article{Highham86,
abstract = {A quadratically convergent Newton method for computing the polar decomposition of a full-rank matrix is presented and analysed. Acceleration parameters are introduced so as to enhance the initial rate of convergence and it is shown how reliable estimates of the optimal parameters may be computed in practice. To add to the known best approximation property of the unitary polar factor, the Hermitian polar factor H of a nonsingular Hermitian matrix A is shown to be a good positive definite approximation to A and 1/2(A / H) is shown to be a best Hermitian positive semi-definite approximation to A. Perturbation bounds for the polar factors are derived. Applications of the polar decomposition to factor analysis, aerospace computations and optimisation are outlined; and a new method is derived for computing the square root of a symmetric positive definite matrix.},
address = {Philadelphia, PA, USA},
author = {Higham, Nicholas J.},


issn = {0196-5204},
journal = {SIAM Journal on Scientific and Statistical Computing},
number = {4},
pages = {1160--1174},
publisher = {Society for Industrial and Applied Mathematics},
title = {{Computing the Polar Decomposition—with Applications}},

volume = {7},
year = {2005}
}
@article{Liu2017,
abstract = {Transfer learning addresses the problem of how to leverage previously acquired knowledge (a source domain) to improve the efficiency of learning in a new domain (the target domain). Although transfer learning has been widely researched in the last decade, existing research still has two restrictions: 1) the feature spaces of the domains must be homogeneous; and 2) the target domain must have at least a few labeled instances. These restrictions significantly limit transfer learning models when transferring knowledge across domains, especially in the big data era. To completely break through both of these bottlenecks, a theorem for reliable unsupervised knowledge transfer is proposed to avoid negative transfers, and a Grassmann manifold is applied to measure the distance between heterogeneous feature spaces. Based on this theorem and the Grassmann manifold, this study proposes two heterogeneous unsupervised knowledge transfer (HeUKT) models – known as RLG and GLG. The random linear monotonic map geodesic flow kernel model (RLG) uses a linear monotonic map (LMM) to reliably project two heterogeneous feature spaces onto a latent feature space and applies geodesic flow kernel (GFK) model to transfers knowledge between two the projected domains. The Grassmann LMM GFK (GLG) optimizes the LMM to achieve the highest possible accuracy and guarantees that the geometric properties of the domains remain unchanged during the transfer process. To test the overall effectiveness of two models, this paper reorganizes five public datasets into ten heterogeneous cross-domain tasks across three application fields: credit assessment, text classification, and cancer detection. Extensive experiments demonstrate that the proposed models deliver superior performance over current benchmarks, and that these HeUKT models are a promising way to give computers the associative ability to judge unknown things using related known knowledge.},
archivePrefix = {arXiv},
arxivId = {1701.02511},
author = {Liu, Feng and Zhang, Guangquan and Lu, Haiyan and Lu, Jie},
eprint = {1701.02511},

journal = {arXiv},
pages = {1--48},
title = {{Heterogeneous Unsupervised Cross-domain Transfer Learning}},
year = {2017}
}
@article{Shao2018,
abstract = {IEEE In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which allows dynamically modeling time-changing concepts, making predictions in a local fashion. Instead of learning a single model on a fixed or adaptive sliding window of historical data or ensemble learning a set of weighted base classifiers, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a proposed P-Tree, which are obtained based on the error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drifts in data streams, PCA and statistical analysis based heuristic approaches have been introduced. To further learn the associations among distributed data streams, the extended P-Tree structure and KNN-style strategy are introduced. We demonstrate that our new data stream classification approach has several attractive benefits: (a) SyncStream is capable of dynamically modeling the evolving concepts from even a small set of prototypes. (b) Owing to synchronization-based constrained clustering and P-Tree, SyncStream supports efficient and effective data representation and maintenance. (c) SyncStream is also tolerant of inappropriate or noisy examples via error-driven representativeness learning. (d) SyncStream allows learning relationship among distributed data streams at the instance level. The experimental results indicate its efficiency and effectiveness.},
author = {Shao, Junming and Huang, Feng and Yang, Qinli and Luo, Guangchun},


issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Data stream,classification,concept drift,synchronization},
number = {5},
pages = {978--991},
title = {{Robust Prototype-Based Learning on Data Streams}},
volume = {30},
year = {2018}
}
@article{LeKander2017,
abstract = {{\textcopyright} 2017 IEEE. Generalized Matrix Learning Vector Quantization (GMLVQ) critically relies on the use of an optimization algorithm to train its model parameters. We test various schemes for automated control of learning rates in gradient-based training. We evaluate these algorithms in terms of their achieved performance and their practical feasibility. We find that some algorithms do indeed perform better than others across multiple benchmark datasets. These algorithms produce GMLVQ models which not only better fit the training data, but also perform better upon validation. In particular, we find that the Variance-based Stochastic Gradient Descent algorithm consistently performs best across all experiments.},
author = {LeKander, Michael and Biehl, Michael and {De Vries}, Harm},


isbn = {9781509066384},
journal = {12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings},
title = {{Empirical evaluation of gradient methods for matrix learning vector quantization}},
year = {2017}
}
@article{Raab2018a,
abstract = {Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning methods are not well suited and used for sparse and interpretable models. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we show how transfer learning can be integrated into the sparse and interpretable probabilistic classification vector machine and it is compared with different standard benchmarks in the field.},
author = {Raab, Christoph and Schleif, Frank-Michael},


issn = {09252312},
journal = {Neurocomputing},
keywords = {Probabilistic classification vec,Transfer learning},
month = {nov},
number = {xxxx},
pages = {187--200},
publisher = {Elsevier B.V.},
title = {{Transfer learning extensions for the probabilistic classification vector machine}},

volume = {91},
year = {2019}
}
@article{Amini2009,
abstract = {We address the problem of learning classifiers when observations$\backslash$r$\backslash$n  have multiple views, some of which may not be observed for all$\backslash$r$\backslash$n  examples.  We assume the existence of view generating functions$\backslash$r$\backslash$n  which may complete the missing views in an approximate way.  This$\backslash$r$\backslash$n  situation corresponds for example to learning text classifiers from$\backslash$r$\backslash$n  multilingual collections where documents are not available in all$\backslash$r$\backslash$n  languages.  In that case, Machine Translation (MT) systems may be$\backslash$r$\backslash$n  used to translate each document in the missing languages.  We derive$\backslash$r$\backslash$n  a generalization error bound for classifiers learned on examples$\backslash$r$\backslash$n  with multiple artificially created views. Our result uncovers a$\backslash$r$\backslash$n  trade-off between the size of the training set, the number of views,$\backslash$r$\backslash$n  and the quality of the view generating functions. As a consequence,$\backslash$r$\backslash$n  we identify situations where it is more interesting to use multiple$\backslash$r$\backslash$n  views for learning instead of classical single view learning.  An$\backslash$r$\backslash$n  extension of this framework is a natural way to leverage unlabeled$\backslash$r$\backslash$n  multi-view data in semi-supervised learning.  Experimental results$\backslash$r$\backslash$n  on a subset of the Reuters RCV1/RCV2 collections support our$\backslash$r$\backslash$n  findings by showing that additional views obtained from MT may$\backslash$r$\backslash$n  significantly improve the classification performance in the cases$\backslash$r$\backslash$n  identified by our trade-off.},
author = {Amini, Massih and Usunier, Nicolas and Goutte, Cyril},

isbn = {9781615679119},
issn = {1615679111},
journal = {Advances in Neural Information Processing Systems 22},
keywords = {Theory {\&} Algorithms},
pages = {28--36},
title = {{Learning from Multiple Partially Observed Views -- an Application to Multilingual Text Categorization}},

year = {2009}
}
@article{Gao2008,
abstract = {Domain Adaptation},
author = {Gao, Jing and Fan, Wei and Jiang, Jing and Han, Jiawei},

isbn = {9781605581934},
journal = {Kdd'08},
pages = {283--291},
title = {{Knowledge Transfer via Multiple Model Local Structure Mapping Categories and Subject Descriptors}},
year = {2008}
}
@article{VanEngelen2019,
abstract = {Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller sets of labelled data. In recent years, research in this area has followed the general trends observed in machine learning, with much attention directed at neural network-based models and generative learning. The literature on the topic has also expanded in volume and scope, now encompassing a broad spectrum of theory, algorithms and applications. However, no recent surveys exist to collect and organize this knowledge, impeding the ability of researchers and engineers alike to utilize it. Filling this void, we present an up-to-date overview of semi-supervised learning methods, covering earlier work as well as more recent advances. We focus primarily on semi-supervised classification, where the large majority of semi-supervised learning research takes place. Our survey aims to provide researchers and practitioners new to the field as well as more advanced readers with a solid understanding of the main approaches and algorithms developed over the past two decades, with an emphasis on the most prominent and currently relevant work. Furthermore, we propose a new taxonomy of semi-supervised classification algorithms, which sheds light on the different conceptual and methodological approaches for incorporating unlabelled data into the training process. Lastly, we show how the fundamental assumptions underlying most semi-supervised learning algorithms are closely connected to each other, and how they relate to the well-known semi-supervised clustering assumption.},
author = {van Engelen, Jesper E. and Hoos, Holger H},


issn = {0885-6125},
journal = {Machine Learning},
keywords = {Classification,Machine learning,Semi-supervised learning,classification,editor,machine learning,semi-supervised learning,tom fawcett},
month = {nov},
pages = {1--68},
publisher = {Springer US},
title = {{A survey on semi-supervised learning}},

year = {2019}
}
@article{Ghifary2017a,
abstract = {This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference between those frameworks is the availability of the unlabeled target data: domain adaptation can leverage unlabeled target information, while domain generalization cannot. We propose Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization. SCA is based on a simple geometrical measure, i.e., scatter, which operates on reproducing kernel Hilbert space. SCA finds a representation that trades between maximizing the separability of classes, minimizing the mismatch between domains, and maximizing the separability of data; each of which is quantified through scatter. The optimization problem of SCA can be reduced to a generalized eigenvalue problem, which results in a fast and exact solution. Comprehensive experiments on benchmark cross-domain object recognition datasets verify that SCA performs much faster than several state-of-the-art algorithms and also provides state-of-the-art classification accuracy in both domain adaptation and domain generalization. We also show that scatter can be used to establish a theoretical generalization bound in the case of domain adaptation.},
archivePrefix = {arXiv},
arxivId = {1510.04373},
author = {Ghifary, Muhammad and Balduzzi, David and Kleijn, W. Bastiaan and Zhang, Mengjie},

eprint = {1510.04373},

issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Domain adaptation,domain generalization,feature learning,kernel methods,object recognition,scatter},
month = {jul},
number = {7},
pages = {1414--1430},
title = {{Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization}},

volume = {39},
year = {2017}
}
@inproceedings{Lutu.2015,
abstract = {{\textcopyright} 2015 IEEE.Data stream mining is the process of applying data mining methods to a data stream in real-Time in order to create descriptive or predictive models. Due to the dynamic nature of data streams, new classes may emerge as a data stream evolves, and the concept being modelled may change with time. This gives rise to the need to continuously make revisions to the predictive model. Revising the predictive model requires that labelled training data should be available. Manual labelling of training data may not be able to cope with the speed at which data needs to be labelled. This paper proposes a predictive modeling framework which supports two of the common decisions that need to be made in stream mining. The framework consists of two components: An online component and an offline component. The online component uses Na{\"{i}}ve Bayes ensemble base models to make predictions for newly arrived data stream instances. The offline component consists of algorithms to combine base model predictions, determine the reliability of the ensemble predictions, select training data for new base models, create new base models, and determine whether the current online base models need to be replaced.},
author = {Lutu, Patricia E.N.},
booktitle = {Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015},


isbn = {9781479975600},
pages = {335--340},
title = {{Na{\"{i}}ve bayes classification ensembles to support modeling decisions in data stream mining}},

year = {2015}
}
@incollection{NIPS2016_6544,
author = {Liu, Ming-Yu and Tuzel, Oncel},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {Lee, D D and Sugiyama, M and Luxburg, U V and Guyon, I and Garnett, R},

pages = {469--477},
publisher = {Curran Associates, Inc.},
title = {{Coupled Generative Adversarial Networks}},

year = {2016}
}
@article{Yanxia2016,
author = {Yanxia, Lv and Cuirong, Wang and Cong, Wang and Bingyu, Liu},


isbn = {9781509036776},
journal = {Proceedings - 2016 International Conference on Advanced Cloud and Big Data, CBD 2016},
keywords = {big data,classification,concept drift,decision tree,uncertain data stream},
pages = {265--271},
title = {{Uncertain Data Stream Classification with Concept Drift}},
year = {2017}
}
@inproceedings{Kumar.2016,
abstract = {Twitter right now gets around 190 million tweets(little content based Web posts) a day, in which individualsshare their remarks with respect to an extensive variety ofsubjects. An expansive number of tweets incorporatesentiments about items and administrations. Notwithstanding, with Twitter being a moderately new wonder, these tweets areunderutilized as a hotspot for assessing client supposition andhave lead specialists to think about the likelihood of their abuseso as to recognize concealed information. Hence, two territoriesare pulling in more enthusiasm for the examination group, thefeeling mining and assessment investigation. We to perform anassessment examination of general's conclusions mined fromthe well known smaller scale blogging site Twitter. The realaccentuation of this paper is set on assessing precision ofvarious machine learning calculations for the errand of twitternotion investigation.},
author = {Kumar, Praveen and Choudhury, Tanupriya and Rawat, Seema and Jayaraman, Shobhna},
booktitle = {Proceedings - 2016 International Conference on Micro-Electronics and Telecommunication Engineering, ICMETE 2016},


isbn = {9781509034116},
keywords = {Machine Learning,Opinion Mining,Sentiment Analysis,Twitter},
month = {sep},
pages = {265--270},
title = {{Analysis of various machine learning algorithms for enhanced opinion mining using twitter data streams}},
year = {2017}
}
@article{Landauer.2008,
abstract = {Latent Semantic Analysis (LSA) was first introduced in Dumais, Furnas, Landauer, and Deerwester ([1988]) and Deerwester, Dumais, Furnas, Landauer, and Harshman ([1990]) as a technique for improving information retrieval. The key insight in LSA was to reduce the dimensionality of the information retrieval problem. Most approaches to retrieving information depend on a lexical match between words in the user's query and those in documents. Indeed, this lexical matching is the way that the popular Web and enterprise search engines work. Such systems are, however, far from ideal. We are all aware of the tremendous amount of irrelevant information that is retrieved when searching. We also fail to find much of the existing relevant material. LSA was designed to address these retrieval problems, using dimension reduction techniques},
author = {Landauer, Thomas K},

journal = {Encyclopedia of Cognitive Science},
number = {11},
pages = {4356},
title = {{Latent Semantic Analysis}},

volume = {3},
year = {2006}
}
@article{Xie2018,
abstract = {Double-stranded {\{}RNA{\}} was identified in six isolates of Tilletia indica, the causal agent of Karnal bunt disease of wheat. Double-stranded {\{}RNA{\}}s ranged from 2.8 to 12.3 kbp in size, similar to sizes reported for double-stranded {\{}RNA{\}}s from other plant pathogenic fungi. The six isolates revealed diverse double-stranded {\{}RNA{\}} banding patterns; however, identical patterns were found for two isolates (one from India, one from Mexico). A random-primed c{\{}DNA{\}} probe generated from the high molecular weight double-stranded {\{}RNA{\}} segment of isolate PV-18 hybridized only to double-stranded {\{}RNA{\}} of isolate PV-18 and not to double-stranded {\{}RNA{\}} of the other five isolates, indicating a high degree of sequence specificity within the double-stranded {\{}RNA{\}} from at least one of the isolates tested. This is the first report of double-stranded {\{}RNA{\}} occurring in any species of Tilletia.},
author = {Beck, Raymond J. and Smith, Oney P. and Tooley, Paul W. and Peterson, Gary L. and Bonde, Morris R.},


isbn = {1529-0131 (Electronic)$\backslash$r0004-3591 (Linking)},
issn = {00275514},
journal = {Mycologia},
number = {5},
pages = {656},
pmid = {23045170},
title = {{Characterization of Double-Stranded RNA from Tilletia indica}},

volume = {86},
year = {1994}
}
@inproceedings{Ghifary2014,
abstract = {We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set.},
address = {Cham},
author = {Ghifary, Muhammad and Kleijn, W Bastiaan and Zhang, Mengjie},
booktitle = {PRICAI 2014: Trends in Artificial Intelligence},
editor = {Pham, Duc-Nghia and Park, Seong-Bae},

isbn = {978-3-319-13560-1},
pages = {898--904},
publisher = {Springer International Publishing},
title = {{Domain Adaptive Neural Networks for Object Recognition}},
year = {2014}
}
@article{Long2015,
abstract = {Domain transfer learning generalizes a learning model across training data and testing data with different distributions. A general principle to tackle this problem is reducing the distribution difference between training data and testing data such that the generalization error can be bounded. Current methods typically model the sample distributions in input feature space, which depends on nonlinear feature mapping to embody the distribution discrepancy. However, this nonlinear feature space may not be optimal for the kernel-based learning machines. To this end, we propose a transfer kernel learning (TKL) approach to learn a domain-invariant kernel by directly matching source and target distributions in the reproducing kernel Hilbert space (RKHS). Specifically, we design a family of spectral kernels by extrapolating target eigensystem on source samples with Mercer's theorem. The spectral kernel minimizing the approximation error to the ground truth kernel is selected to construct domain-invariant kernel machines. Comprehensive experimental evidence on a large number of text categorization, image classification, and video event recognition datasets verifies the effectiveness and efficiency of the proposed TKL approach over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]},
author = {Long, Mingsheng and Wang, Jianmin and Sun, Jiaguang and Yu, Philip S.},


isbn = {1041-4347},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Nystrom method,Transfer learning,image classification,kernel learning,text mining,video recognition},
number = {6},
pages = {1519--1532},
title = {{Domain invariant transfer kernel learning}},
volume = {27},
year = {2015}
}
@inproceedings{Sun_2019_CVPR,
author = {Sun, Qianru and Liu, Yaoyao and Chua, Tat-Seng and Schiele, Bernt},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{Meta-Transfer Learning for Few-Shot Learning}},
year = {2019}
}
@article{Fernando2013a,
abstract = {In this paper, we introduce a new domain adaptation (DA) algorithm $\backslash$nwhere the source and target domains are represented by subspaces described by $\backslash$neigenvectors. In this context, our method seeks a domain adaptation solution by $\backslash$nlearning a mapping function which aligns the source subspace with the target $\backslash$none. We show that the solution of the corresponding optimization problem can be $\backslash$nobtained in a simple closed form, leading to an extremely fast algorithm. We use $\backslash$na theoretical result to tune the unique hyper parameter corresponding to the $\backslash$nsize of the subspaces. We run our method on various datasets and show that, $\backslash$ndespite its intrinsic simplicity, it outperforms state of the art DA methods.},
author = {Fernando, Basura and Habrard, Amaury and Sebban, Marc and Tuytelaars, Tinne},


isbn = {9781479928392},
issn = {1550-5499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
keywords = {domain adaptation,object recognition,subspace alignment},
pages = {2960--2967},
pmid = {6751479},
title = {{Unsupervised visual domain adaptation using subspace alignment}},
year = {2013}
}
@article{Long2013a,
abstract = {Transfer learning is established as an effective technology in $\backslash$ncomputer vision for leveraging rich labeled data in the source domain to build $\backslash$nan accurate classifier for the target domain. However, most prior methods have $\backslash$nnot simultaneously reduced the difference in both the marginal distribution and $\backslash$nconditional distribution between domains. In this paper, we put forward a novel $\backslash$ntransfer learning approach, referred to as Joint Distribution Adaptation (JDA). $\backslash$nSpecifically, JDA aims to jointly adapt both the marginal distribution and $\backslash$nconditional distribution in a principled dimensionality reduction procedure, and $\backslash$nconstruct new feature representation that is effective and robust for $\backslash$nsubstantial distribution difference. Extensive experiments verify that JDA can $\backslash$nsignificantly outperform several state-of-the-art methods on four types of $\backslash$ncross-domain image classification problems.},
author = {Long, Mingsheng and Wang, Jianmin and Ding, Guiguang and Sun, Jiaguang and Yu, Philip S.},


isbn = {9781479928392},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
keywords = {Transfer learning,feature learning,joint distribution adaptation},
pages = {2200--2207},
publisher = {IEEE},
title = {{Transfer feature learning with joint distribution adaptation}},
year = {2013}
}
@inproceedings{DBLP:conf/icml/DuanXT12,
abstract = {We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods.},
archivePrefix = {arXiv},
arxivId = {1206.4660},
author = {Duan, Lixin and Xu, Dong and Tsang, Ivor},
booktitle = {Proceedings of the 29th International Conference on Machine Learning, ICLR 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012},
eprint = {1206.4660},

title = {{Learning with Augmented Features for Heterogeneous Domain Adaptation}},

year = {2012}
}
@article{Liu2013,
author = {Liu, Bo and Yu, Philip S. and Xiao, Yanshan and Hao, Zhifeng},


isbn = {9783642374524},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Data of uncertainty,One-class learning,Transfer learning},
number = {PART 1},
pages = {471--483},
title = {{One-class transfer learning with uncertain data}},
volume = {7818 LNAI},
year = {2013}
}
@article{Alex2009,
abstract = {The presence of huge data sets poses new problems to popular clustering and visualization algorithms such as neural gas (NG) and the self-organising-map (SOM) due to memory and time constraints. In such situations, it is no longer possible to store all data points in the main memory at once and only a few, ideally only one run over the whole data set is still affordable to achieve a feasible training time. In this contribution we propose single pass extensions of the classical clustering algorithms NG and SOM which are based on a simple patch decomposition of the data set and fast batch optimization schemes of the underlying cost function. The algorithms only require a fixed memory space. They maintain the benefits of the original ones including easy implementation and interpretation as well as large flexibility and adaptability. We demonstrate that parallelization of the methods becomes easily possible and we show the efficiency of the approach in a variety of experiments. {\textcopyright} 2009 Elsevier B.V. All rights reserved.},
author = {Alex, Nikolai and Hasenfuss, Alexander and Hammer, Barbara},


issn = {09252312},
journal = {Neurocomputing},
keywords = {Clustering streaming data,Neural gas,Parallelization},
number = {7-9},
pages = {1455--1469},
title = {{Patch clustering for massive data sets}},
volume = {72},
year = {2009}
}
@article{Kirstein2008,
abstract = {We present a biologically motivated architecture for object recognition that is based on a hierarchical feature-detection model in combination with a memory architecture that implements short-term and long-term memory for objects. A particular focus is the functional realization of online and incremental learning for the task of appearance-based object recognition of many complex-shaped objects. We propose some modifications of learning vector quantization algorithms that are especially adapted to the task of incremental learning and capable of dealing with the stability-plasticity dilemma of such learning algorithms. Our technical implementation of the neural architecture is capable of online learning of 50 objects within less than three hours. {\textcopyright} 2007 Elsevier Ltd. All rights reserved.},
author = {Kirstein, Stephan and Wersing, Heiko and K{\"{o}}rner, Edgar},


issn = {08936080},
journal = {Neural Networks},
keywords = {Hierarchical feature extraction,Incremental and life-long learning,Learning vector quantization,Stability-plasticity dilemma},
number = {1},
pages = {65--77},
pmid = {18182276},
title = {{A biologically motivated visual memory architecture for online learning of objects}},
volume = {21},
year = {2008}
}
@inproceedings{DBLP:conf/cvpr/RedmonDGF16,
abstract = {We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.},
author = {Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},


isbn = {9781467388504},
issn = {10636919},
month = {jun},
pages = {779--788},
publisher = {IEEE},
title = {{You only look once: Unified, real-time object detection}},

volume = {2016-Decem},
year = {2016}
}
@inproceedings{Bui.2017,
abstract = {Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the hyperparameter estimates are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing methods for learning hyperparameters and optimising pseudo-input locations. The proposed framework is assessed using synthetic and real-world datasets.},
archivePrefix = {arXiv},
arxivId = {1705.07131},
author = {Bui, Thang D. and Nguyen, Cuong V. and Turner, Richard E.},
booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, {\{}USA{\}}},
eprint = {1705.07131},

pages = {3301--3309},
title = {{Streaming Sparse Gaussian Process Approximations}},

year = {2017}
}
@article{Fayed2007,
abstract = {Prototype classifiers are a type of pattern classifiers, whereby a number of prototypes are designed for each class so as they act as representatives of the patterns of the class. Prototype classifiers are considered among the simplest and best performers in classification problems. However, they need careful positioning of prototypes to capture the distribution of each class region and/or to define the class boundaries. Standard methods, such as learning vector quantization (LVQ), are sensitive to the initial choice of the number and the locations of the prototypes and the learning rate. In this article, a new prototype classification method is proposed, namely self-generating prototypes (SGP). The main advantage of this method is that both the number of prototypes and their locations are learned from the training set without much human intervention. The proposed method is compared with other prototype classifiers such as LVQ, self-generating neural tree (SGNT) and K-nearest neighbor (K-NN) as well as Gaussian mixture model (GMM) classifiers. In our experiments, SGP achieved the best performance in many measures of performance, such as training speed, and test or classification speed. Concerning number of prototypes, and test classification accuracy, it was considerably better than the other methods, but about equal on average to the GMM classifiers. We also implemented the SGP method on the well-known STATLOG benchmark, and it beat all other 21 methods (prototype methods and non-prototype methods) in classification accuracy. {\textcopyright} 2006 Pattern Recognition Society.},
author = {Fayed, Hatem A. and Hashem, Sherif R. and Atiya, Amir F.},


issn = {00313203},
journal = {Pattern Recognition},
keywords = {Gaussian mixture models,Learning vector quantization,Nearest neighbor,Prototype classifiers,Self-generating neural trees},
number = {5},
pages = {1498--1509},
title = {{Self-generating prototypes for pattern classification}},
volume = {40},
year = {2007}
}
@inproceedings{10.1007/978-3-030-10928-8_47,
abstract = {Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes that source domains are richly or at least comparably labeled. While this strong assumption is never true in practice, this paper relaxes it and addresses challenges related to sources with diverse labeling volume and diverse reliability. The first challenge is combining domain similarity and source reliability by proposing a new transfer learning method that utilizes both source-target similarities and inter-source relationships. The second challenge involves pool-based active learning where the oracle is only available in source domains, resulting in an integrated active transfer learning framework that incorporates distribution matching and uncertainty sampling. Extensive experiments on synthetic and two real-world datasets clearly demonstrate the superiority of our proposed methods over several baselines including state-of-the-art transfer learning methods.},
address = {Cham},
author = {Wang, Zirui and Carbonell, Jaime},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

editor = {Berlingerio, Michele and Bonchi, Francesco and G{\"{a}}rtner, Thomas and Hurley, Neil and Ifrim, Georgiana},

isbn = {9783030109271},
issn = {16113349},
pages = {794--810},
publisher = {Springer International Publishing},
title = {{Towards more reliable transfer learning}},
volume = {11052 LNAI},
year = {2019}
}
@inproceedings{Zellinger2017,
annote = {The most successful methods have in common that they encourage similarity between the latent network representations w. r. t. the different domains. This similarity is often enforced by minimizing a certain distance between the networks' domain- specific hidden activations.

Two of them, the MMD and the KL-divergence approach, can be viewed as the matching of statis- tical moments. The KL-divergence approach is based on mean (first raw moment) matching. Using the Taylor expansion of the Gaussian kernel, most MMD-based approaches can be viewed as mini- mizing a certain distance between weighted sums of all raw moments (Li

The two approaches MMD and the Proxy A-distance have in common that they do not minimize the domain discrepancy explicitly in the hidden activation space. In},
author = {Zellinger, Werner and Grubinger, Thomas and Lughofer, Edwin and Natschl{\"{a}}ger, Thomas and Saminger-Platz, Susanne},
booktitle = {5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings},

title = {{Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning}},

year = {2017}
}
@inproceedings{Bifet2009,
abstract = {We propose and illustrate a method for developing algorithms that can adaptively learn from data streams that drift over time. As an example, we take Hoeffding Tree, an incremental decision tree inducer for data streams, and use as a basis it to build two new methods that can deal with distribution and concept drift: a sliding window-based algorithm, Hoeffding Window Tree, and an adaptive method, Hoeffding Adaptive Tree. Our methods are based on using change detectors and estimator modules at the right places; we choose implementations with theoretical guarantees in order to extend such guarantees to the resulting adaptive learning algorithm. A main advantage of our methods is that they require no guess about how fast or how often the stream will drift; other methods typically have several user-defined parameters to this effect.},
address = {Berlin, Heidelberg},
annote = {Lemma},
author = {Bifet, Albert and Gavald{\`{a}}, Ricard and Bifet, Albert and Gavald, Ricard},
booktitle = {Advances in Intelligent Data Analysis VIII},


isbn = {9783642039157},
number = {September},
pages = {249--260},
publisher = {Springer Berlin Heidelberg},
title = {{Adaptive Learning from Evolving Data Streams Adaptive Parameter-free Learning from Evolving Data Streams}},
year = {2017}
}
@article{Kodirov2015,
abstract = {{\textcopyright} 2015 IEEE.Zero-shot learning (ZSL) can be considered as a special case of transfer learning where the source and target domains have different tasks/label spaces and the target domain is unlabelled, providing little guidance for the knowledge transfer. A ZSL method typically assumes that the two domains share a common semantic representation space, where a visual feature vector extracted from an image/video can be projected/embedded using a projection function. Existing approaches learn the projection function from the source domain and apply it without adaptation to the target domain. They are thus based on naive knowledge transfer and the learned projections are prone to the domain shift problem. In this paper a novel ZSL method is proposed based on unsupervised domain adaptation. Specifically, we formulate a novel regularised sparse coding framework which uses the target domain class labels' projections in the semantic space to regularise the learned target domain projection thus effectively overcoming the projection domain shift problem. Extensive experiments on four object and action recognition benchmark datasets show that the proposed ZSL method significantly outperforms the state-of-the-arts.},
annote = {Zero Shot Learning = Labels of Target and Source are different
Dictionary Learning {\~{}} Subspace Projection as Semantic Embedding and vectors represent dictrionary entries. No Orthogonal contraints.

Least-Square with regularization and sparse coding.
Regularization = Control squared frobenious norm of Dict-Matrix with Parameter
Sparse = inequality contraint for restricting a single dict vector to length maximum 1.
To avoid trivial solutions = Regularization Term

Ds Embedding found via closed form

Dt-Projection,Yt-Embedding:
Regularization term adapted to be close to source dictonary Ds-Dt
Pseudo Labelling via similarity prototpye classification (Indirect atrribute prediction). Clustering in subspace with probability of beeing the actural label and subspace distance

Learning:
Dt und Yt learned via EM-Like learning. Fix Subspace Embedding Ys update Subspace Projection
Switch - repeat til convergence},
author = {Kodirov, Elyor and Xiang, Tao and Fu, Zhenyong and Gong, Shaogang},


isbn = {9781467383912},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {2452--2460},
publisher = {IEEE},
title = {{Unsupervised domain adaptation for zero-shot learning}},
volume = {2015 Inter},
year = {2015}
}
@inproceedings{Bifet:2017:EFD:3097983.3098139,
abstract = {Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called streamDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. streamDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision trees. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources.},
address = {New York, NY, USA},
author = {Bifet, Albert and Zhang, Jiajin and Fan, Wei and He, Cheng and Zhang, Jianfeng and Qian, Jianfeng and Holmes, Geoff and Pfahringer, Bernhard},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},


isbn = {978-1-4503-4887-4},
keywords = {classification,data streams,decision trees,online learning},
pages = {1733--1742},
publisher = {ACM},
series = {KDD '17},
title = {{Extremely Fast Decision Tree Mining for Evolving Data Streams}},

year = {2017}
}
@article{Zarrabi-Zadeh2006,
abstract = {We analyze an extremely simple approximation algorithm for computing the minimum enclosing ball (or the 1-center) of a set of points in high dimensions. We prove that this algorithm computes a 3/2-factor approximation in any dimension using minimum space in just one pass over the data points.},
author = {Zarrabi-Zadeh, Hamid and Chan, Timothy M.},


journal = {Proc. 18th Annual Canadian Conf. Comput. {\ldots}},
pages = {14--17},
title = {{A simple streaming algorithm for minimum enclosing balls}},

year = {2006}
}
@article{JMLR:v19:18-251,
abstract = {Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality is enforced by complying with PEP8 guidelines and using continuous integration and automatic testing. The source code is publicly available at https://github.com/scikit-multiflow/scikit-multiflow.},
archivePrefix = {arXiv},
arxivId = {1807.04662},
author = {Montiel, Jacob and Read, Jesse and Bifet, Albert and Abdessalem, Talel},
eprint = {1807.04662},
journal = {Journal of Machine Learning Research},
number = {72},
pages = {1--5},
title = {{Scikit-Multiflow: A Multi-output Streaming Framework}},

volume = {19},
year = {2018}
}
@inproceedings{Wang2018,
author = {Wang, Shanshan and Zhang, Lei and Zuo, Wangmeng},
booktitle = {2017 IEEE International Conference on Computer Vision Workshops (ICCVW)},


isbn = {978-1-5386-1034-3},
month = {oct},
pages = {949--957},
publisher = {IEEE},
title = {{Class-Specific Reconstruction Transfer Learning via Sparse Low-Rank Constraint}},

volume = {2018-Janua},
year = {2017}
}
@incollection{NIPS2019_9249,
author = {Wang, Boyu and Mendez, Jorge and Cai, Mingbo and Eaton, Eric},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {10644--10654},
publisher = {Curran Associates, Inc.},
title = {{Transfer Learning via Minimizing the Performance Gap Between Domains}},

year = {2019}
}
@article{Wu2018a,
abstract = {In recent years, graph based subspace clustering has attracted considerable attentions in computer vision, as its capability of clustering data efficiently. However, the graph weights built by using representation coefficients are not the exact ones as the traditional definition. That is, the two steps are conducted in independent manner such that an overall optimal result cannot be guaranteed. To this end, in this paper, a novel subspace clustering via learning an adaptive graph affinity matrix is proposed, where the soft label and the representation coefficients of data are learned in an unified framework. First, the proposed method learns a robust representation for the data through least square regression, which reveals the subspace structure within data and captures various noises inside. Second, the segmentation is sought by conducting spectral clustering simultaneously. Most importantly, during the optimization process, the segmentation is utilized to iteratively enhance the block-diagonal structure of the learned representation to further assist the clustering process. Experimental results on several famous databases demonstrate that the proposed method performs better against the state-of-the-art approaches, in clustering.},
author = {Wu, Zongze and Yin, Ming and Zhou, Yajing and Fang, Xiaozhao and Xie, Shengli},


issn = {1370-4621},
journal = {Neural Processing Letters},
keywords = {Affinity matrix,Least square regression,Sparse representation,Spectral clustering,Subspace segmentation},
month = {dec},
number = {3},
pages = {1359--1372},
publisher = {Springer US},
title = {{Robust Spectral Subspace Clustering Based on Least Square Regression}},

volume = {48},
year = {2018}
}
@article{Lee2013,
abstract = {We propose the simple and efficient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised fashion with labeled and unlabeled data simultaneously. For un-labeled data, Pseudo-Labels, just picking up the class which has the maximum predicted probability, are used as if they were true labels. This is in effect equivalent to Entropy Regularization. It favors a low-density separation between classes, a commonly assumed prior for semi-supervised learning. With Denoising Auto-Encoder and Dropout, this simple method outperforms conventional methods for semi-supervised learning with very small labeled data on the MNIST handwritten digit dataset.},
author = {Lee, Dong-Hyun},

journal = {ICML 2013 Workshop: Challenges in Representation Learning},
pages = {1--6},
title = {{Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks}},

year = {2013}
}
@incollection{Ding2018,
abstract = {Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches.},
author = {Ding, Zhengming and Li, Sheng and Shao, Ming and Fu, Yun},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},


isbn = {9783030012151},
issn = {16113349},
keywords = {Adaptive graph,Domain adaptation,Semi-supervised learning},
pages = {36--52},
title = {{Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation}},

volume = {11206 LNCS},
year = {2018}
}
@article{Ding2017,
abstract = {Metric learning has attracted increasing attention due to its critical role in image analysis and classification. Conventional metric learning always assumes that the training and test data are sampled from the same or similar distribution. However, to build an effective distance metric, we need abundant supervised knowledge (i.e., side/label information), which is generally inaccessible in practice, because of the expensive labeling cost. In this paper, we develop a robust transfer metric learning (RTML) framework to effectively assist the unlabeled target learning by transferring the knowledge from the well-labeled source domain. Specifically, RTML exploits knowledge transfer to mitigate the domain shift in two directions, i.e., sample space and feature space. In the sample space, domain-wise and class-wise adaption schemes are adopted to bridge the gap of marginal and conditional distribution disparities across two domains. In the feature space, our metric is built in a marginalized denoising fashion and low-rank constraint, which make it more robust to tackle noisy data in reality. Furthermore, we design an explicit rank constraint regularizer to replace the rank minimization NP-hard problem to guide the low-rank metric learning. Experimental results on several standard benchmarks demonstrate the effectiveness of our proposed RTML by comparing it with the stateof- the-art transfer learning and metric learning algorithms.},
author = {Ding, Zhengming and Fu, Yun},


issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Transfer learning,cross-domain metric,marginalized denoising},
number = {2},
pages = {660--670},
pmid = {27893391},
title = {{Robust Transfer Metric Learning for Image Classification}},
volume = {26},
year = {2017}
}
@inproceedings{10.5555/2980539.2980649,
address = {Cambridge, MA, USA},
author = {Ng, Andrew Y and Jordan, Michael I and Weiss, Yair},
booktitle = {Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic},

pages = {849--856},
publisher = {MIT Press},
series = {NIPS'01},
title = {{On Spectral Clustering: Analysis and an Algorithm}},
year = {2001}
}
@article{Musco2016,
abstract = {We give the first algorithm for kernel Nystr$\backslash$"om approximation that runs in *linear time in the number of training points* and is provably accurate for all kernel matrices, without dependence on regularity or incoherence conditions. The algorithm projects the kernel onto a set of {\$}s{\$} landmark points sampled by their *ridge leverage scores*, requiring just {\$}O(ns){\$} kernel evaluations and {\$}O(ns{\^{}}2){\$} additional runtime. While leverage score sampling has long been known to give strong theoretical guarantees for Nystr$\backslash$"om approximation, by employing a fast recursive sampling scheme, our algorithm is the first to make the approach scalable. Empirically we show that it finds more accurate, lower rank kernel approximations in less time than popular techniques such as uniformly sampled Nystr$\backslash$"om approximation and the random Fourier features method.},
archivePrefix = {arXiv},
arxivId = {1605.07583},
author = {Musco, Cameron and Musco, Christopher},
eprint = {1605.07583},

journal = {Advances in Neural Information Processing Systems},
number = {Nips},
title = {{Recursive Sampling for the Nystr$\backslash$"om Method}},

year = {2016}
}
@article{Gunn.2018,
abstract = {Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future.},
author = {Gunn, Iain A.D. and Arnaiz-Gonz{\'{a}}lez, {\'{A}}lvar and Kuncheva, Ludmila I.},


issn = {18728286},
journal = {Neurocomputing},
keywords = {Concept drift,Instance selection,Machine learning,Prototype generation,Stream classification},
pages = {167--178},
title = {{A taxonomic look at instance-based stream classifiers}},

volume = {286},
year = {2018}
}
@article{Lughofer2008,
abstract = {In this paper, we extend the conventional vector quantization by incorporating a vigilance parameter, which steers the tradeoff between plasticity and stability during incremental online learning. This is motivated in the adaptive resonance theory (ART) network approach and is exploited in our paper for forming a one-pass incremental and evolving variant of vector quantization. This variant can be applied for online clustering, classification and approximation tasks with an unknown number of clusters. Additionally, two novel extensions are described: one concerns the incorporation of the sphere of influence of clusters in the vector quantization learning process by selecting the 'winning cluster' based on the distances of a data point to the surface of all clusters. Another one introduces a deletion of cluster satellites and an online split-and-merge strategy: clusters are dynamically split and merged after each incremental learning step. Both strategies prevent the algorithm to generate a wrong cluster partition due to a bad a priori setting of the most essential parameter(s). The extensions will be applied to clustering of two- and high-dimensional data, within an image classification framework and for model-based fault detection based on data-driven evolving fuzzy models. {\textcopyright} 2007 Pattern Recognition Society.},
author = {Lughofer, Edwin},


isbn = {0031-3203},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Clustering,Evolving fuzzy models,Fault detection,Image classification framework,Incremental learning,New winning cluster selection strategy,Removing cluster satellites,Split-and-merge strategy,Vector quantization},
number = {3},
pages = {995--1011},
title = {{Extensions of vector quantization for incremental clustering}},
volume = {41},
year = {2008}
}
@inproceedings{pmlr-v97-chen19i,
abstract = {Adversarial domain adaptation has made remarkable advances in learning transferable representations for knowledge transfer across domains. While adversarial learning strengthens the feature transferability which the community focuses on, its impact on the feature discriminability has not been fully explored. In this paper, a series of experiments based on spectral analysis of the feature representations have been conducted, revealing an unexpected deterioration of the discriminability while learning transferable features adversarially. Our key finding is that the eigenvectors with the largest singular values will dominate the feature transferability. As a consequence, the transferability is enhanced at the expense of over penalization of other eigenvectors that embody rich structures crucial for discriminability. Towards this problem, we present Batch Spectral Penalization (BSP), a general approach to penalizing the largest singular values so that other eigenvectors can be relatively strengthened to boost the feature discriminability. Experiments show that the approach significantly improves upon representative adversarial domain adaptation methods to yield state of the art results.},
address = {Long Beach, California, USA},
annote = {Principle: Larger Singular values should be lowered in value so that the power distriution of the spectrum is flattend as curve.

Large singular values carrry more transferable knowledge than smaller ones which may endorse domain specific variances.
However, that not only the largest singular values are used in the learning process which are consituted to be the discriminative information one wants the larger singular values to be more aligned with the mid-sized singular values.
Smallest ones are domain variant features and regarded as non transferable.

LDA: Linear Discriminat Analysis for between class and within class variance:
The other is discriminability, which refers to the easiness of separating different categories by a supervised classifier trained over the feature representations.

Definition Transferability:
One is transferability, which indicates the ability of feature repre- sentations to bridge the discrepancy across domains.

Definition Discriminability:
The other is discriminability, which refers to the easiness of separating different categories by a supervised classifier trained over the feature representations.

Approaches in Deep Domain Adaptation:
The first strategy is moment matching, bridging the distributions by matching all their statistics.

The second strategy is adversarial learning, where a domain discriminator is trained to distinguish the source from the target while feature representations are learned to confuse it simultaneously

Plots:
SIngular Values. Angle of SIngular Vectors
Error Rate and A-Distance ( d{\_}a = 2(1-2e), where e i domain confusion error.},
author = {Chen, Xinyang and Wang, Sinan and Long, Mingsheng and Wang, Jianmin},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},

pages = {1081--1090},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation}},

volume = {97},
year = {2019}
}
@inproceedings{Wang.2003,
abstract = {An effective processing and analysis of data streams is of utmost importance for a plethora of emerging applications like network monitoring, traffic management, and financial tickers. In addition to the management of transient and potentially unbounded streams, their analysis with advanced data mining techniques has been identified as a research challenge. A wellestablished class of mining techniques is based on nonparametric statistics where especially nonparametric density estimation is among the essential building blocks. In this paper, we examine the maintenance of nonparametric estimators over data streams. We present a tailored framework that incrementally maintains a nonparametric estimator over a data stream while consuming only a fixed amount of memory. Our framework is memory-adaptive and therefore, supports a fundamental requirement for an operator within a data stream management system. As an example, we apply our framework to selectivity estimation of range queries, which is a popular use-case for statistical estimators. After providing an analysis of the processing cost, results of experimental comparisons are reported where synthetic data streams as well as real-world ones are considered. Our results demonstrate the accuracy of the results being produced by estimators derived from our framework.},
author = {Wang, Haixun and Fan, Wei and Yu, Philip S. and Han, Jiawei},
booktitle = {Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining  - KDD '03},


isbn = {1581137370},
pages = {226},
title = {{Mining concept-drifting data streams using ensemble classifiers}},

year = {2003}
}
@article{JialinLiuGuYuantao2015,
author = {{Jialin, Liu, Gu, Yuantao}, Wang Mengdi},

isbn = {9781467369978},
journal = {{\{}IEEE{\}} International Conference on Acoustics, Speech and Signal Processing, {\{}ICASSP{\}} 2015},
pages = {3586--3590},
title = {{AVERAG ING RA N DOM PROJECTION: A FAST ON L INE SOLUTION F OR L A RGE-SCA LE CONSTRAINED STOCHASTIC OPTIMIZATION Jialin Liu, Yuantao Gu*, and Mengdi Wangt}},
year = {2015}
}

@inproceedings{Wang:2011:HDA:2283516.2283652,
abstract = {We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications.},
author = {Wang, Chang and Mahadevan, Sridhar},
booktitle = {IJCAI International Joint Conference on Artificial Intelligence},


isbn = {9781577355120},
issn = {10450823},
pages = {1541--1546},
publisher = {AAAI Press},
series = {IJCAI'11},
title = {{Heterogeneous domain adaptation using manifold alignment}},

year = {2011}
}
@inproceedings{raab18a,
abstract = {Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning methods are not well suited and used for sparse and interpretable models. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we show how transfer learning can be integrated into the sparse and interpretable probabilistic classification vector machine and it is compared with different standard benchmarks in the field.},
author = {Raab, Christoph and Schleif, Frank-Michael},
booktitle = {Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications},
editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni and Peeters, Ralf},

pages = {187--200},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Transfer learning for the probabilistic classification vector machine}},

volume = {91},
year = {2018}
}
@inproceedings{Wang2019,
author = {Wang, Yifan and Huang, Junchu and Shang, Junyuan and Niu, Chang and Zhou, Zhiheng},
booktitle = {2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS)},


isbn = {978-1-5386-9273-8},
keywords = {Distribution matching,Heterogeneous domain adaptation,Structure consistency,Transfer learning},
month = {dec},
pages = {227--231},
publisher = {IEEE},
title = {{Domain Invariant and Class Discriminative Heterogeneous Domain Adaptation}},

year = {2018}
}
@article{Glorot2011,
abstract = {The exponential increase in the availability of online reviews and recommendations makes sentiment classi cation an interesting topic in academic and industrial research. Reviews can span so many di erent domains that it is dicult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classi ers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classi ers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains.},
author = {Glorot, Xavier and Bordes, Antoine and Bengio, Yoshua},

isbn = {978-1-4503-0619-5},
journal = {Proceedings of the 28th International Conference on Machine Learning},
number = {1},
pages = {513--520},
pmid = {2410837},
title = {{Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach}},

year = {2011}
}
@article{Figueras1993,
abstract = {Graph theoretic analysis demonstrates that the Morgan algorithm is equivalent to computing a power of the adjacency matrix. {\textcopyright} 1993, American Chemical Society. All rights reserved.},
author = {Figueras, John},

issn = {1549-9596},
journal = {Journal of Chemical Information and Modeling},
month = {sep},
number = {5},
pages = {717--718},
title = {{Morgan revisited}},

volume = {33},
year = {1993}
}
@inproceedings{Lee_2019_ICCV,
author = {Lee, Seungmin and Kim, Dongwan and Kim, Namil and Jeong, Seong-Gyun},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},

title = {{Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation}},
year = {2019}
}
@article{Biehl2006,
author = {Biehl, Michael and Hammer, Barbara},

journal = {Computer},
title = {{Matrix learning in learning vector quantization}},

year = {2006}
}
@article{Borgwardt2006,
abstract = {Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. Results: We study the practical feasibility of an MM D-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. Conclusions: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments. {\textcopyright} 2006 Oxford University Press.},
author = {Borgwardt, Karsten M. and Gretton, Arthur and Rasch, Malte J. and Kriegel, H.-P. and Scholkopf, B. and Smola, Alex J.},


issn = {1367-4803},
journal = {Bioinformatics},
month = {jul},
number = {14},
pages = {e49--e57},
title = {{Integrating structured biological data by Kernel Maximum Mean Discrepancy}},

volume = {22},
year = {2006}
}
@inproceedings{8757975,
author = {Osumi, Kosuke and Yamashita, Takayoshi and Fujiyoshi, Hironobu},
booktitle = {2019 16th International Conference on Machine Vision Applications (MVA)},


number = {1},
pages = {1--5},
title = {{Domain Adaptation using a Gradient Reversal Layer with Instance Weighting}},
year = {2019}
}
@inproceedings{Masud.2009,
abstract = {We propose a multi-partition, multi-chunk ensemble classifier based data mining technique to classify concept-drifting data streams. Ex-isting ensemble techniques in classifying concept-drifting data streams fol-low a single-partition, single-chunk approach, in which a single data chunk is used to train one classifier. In our approach, we train a collection of v classifiers from r consecutive data chunks using v-fold partitioning of the data, and build an ensemble of such classifiers. By introducing this multi-partition, multi-chunk ensemble technique, we significantly reduce clas-sification error compared to the single-partition, single-chunk ensemble approaches. We have theoretically justified the usefulness of our algorithm, and empirically proved its effectiveness over other state-of-the-art stream classification techniques on synthetic data and real botnet traffic.},
author = {Masud, Mohammad M. and Gao, Jing and Khan, Latifur and Han, Jiawei and Thuraisingham, Bhavani},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},


isbn = {3642013066},
issn = {03029743},
pages = {363--375},
title = {{A multi-partition multi-chunk ensemble technique to classify concept-drifting data streams}},

volume = {5476 LNAI},
year = {2009}
}
@incollection{Cornuejols2010,
abstract = {After a long period of neglect, on-line learning is re-emerging as an important topic in machine learning. On one hand, this is due to new applications involving data flows, the detection of, or adaption to, changing conditions and long-life learning. On the other hand, it is now apparent that the current statistical theory of learning, based on the independent and stationary distribution assumption, has reached its limits and must be completed or superseded to account for sequencing effects, and more generally, for the information carried by the evolution of the data generation process. This chapter first presents the current, still predominant paradigm. It then underlines the deviations to this framework introduced by new on-line learning settings, and the associated challenges that they raise both for devising novel algorithms and for developing a satisfactory new theory of learning. It concludes with a brief description of a new learning concept, called tracking, which may hint as to what could come off as algorithms and theoretical questions from looking anew to this all pervading situation: never to stop learning. {\textcopyright} 2010 Springer-Verlag.},
author = {Cornu{\'{e}}jols, Antoine},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},


isbn = {3642163912},
issn = {03029743},
pages = {129--147},
title = {{On-Line Learning: Where Are We So Far?}},

volume = {6202 LNAI},
year = {2010}
}
@article{Rousselle2015,
abstract = {Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own specificities. Among the many strategies proposed to adapt a domain to another, finding a common representation has shown excellent properties: by finding a common representation for both domains, a single classifier can be effective in both and use labelled samples from the source domain to predict the unlabelled samples of the target domain. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labelled samples in the source domain to remain close during transport. This way, we exploit at the same time the few labeled information in the source and the unlabelled distributions observed in both domains. Experiments in toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches.},
archivePrefix = {arXiv},
arxivId = {1507.00504},
author = {Courty, Nicolas and Flamary, Remi and Tuia, Devis and Rakotomamonjy, Alain},

eprint = {1507.00504},

isbn = {9782875870148},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Unsupervised domain adaptation,classification,optimal transport,transfer learning,visual adaptation},
number = {9},
pages = {1853--1865},
title = {{Optimal Transport for Domain Adaptation}},

volume = {39},
year = {2017}
}
@article{Schoenemann1966,
abstract = {A solutionT of the least-squares problemAT=B +E, givenA andB so that trace (E{\{}$\backslash$textasciiacutex{\}}E)= minimum andT{\{}$\backslash$textasciiacutex{\}}T=I is presented. It is compared with a less general solution of the same problem which was given by Green [5]. The present solution, in contrast to Green's, is applicable to matricesA andB which are of less than full column rank. Some technical suggestions for the numerical computation ofT and an illustrative example are given.},
author = {Sch{\"{o}}nemann, Peter H},


issn = {1860-0980},
journal = {Psychometrika},
number = {1},
pages = {1--10},
title = {{A generalized solution of the orthogonal procrustes problem}},

volume = {31},
year = {1966}
}
@article{Margolis2017,
abstract = {Objective. To determine instructional best practice recommendations for use of blended learning from the students' perspective. Methods. Three focus groups were created, one for each of the first three years at a school of pharmacy. The focus group discussions were audio recorded and transcribed for content analysis. Results. Ten instructional best practices were identified from the focus groups: setting the stage, consistency when team teaching, timeliness in posting materials, time on task, accountability for online activities, use of structured active learning, instructor use of feedback on student preparation, incorporation of student feedback into the course, short reviews of online material during class, and ensuring technologies are user friendly. Conclusion. Instructors using blended learning should consider incorporating these best practices into their course design and management. More evaluation is needed to see if implementation of these practices affects student performance.},
author = {Margolis, Amanda R. and Porter, Andrea L. and Pitterle, Michael E.},


issn = {15536467},
journal = {American Journal of Pharmaceutical Education},
keywords = {Active learning,Blended learning,Flipped classroom,Instructional design},
number = {3},
pages = {49},
pmid = {28496269},
title = {{Best practices for use of blended learning}},
volume = {81},
year = {2017}
}
@article{Gomes.2017,
abstract = {Ensemble-based methods are among the most widely used techniques for data stream classification. Their popularity is attributable to their good performance in comparison to strong single learners while being relatively easy to deploy in real-world applications. Ensemble algorithms are especially useful for data stream learning as they can be integrated with drift detection algorithms and incorporate dynamic updates, such as selective removal or addition of classifiers. This work proposes a taxonomy for data stream ensemble learning as derived from reviewing over 60 algorithms. Important aspects such as combination, diversity, and dynamic updates, are thoroughly discussed. Additional contributions include a listing of popular opensource tools and a discussion about current data stream research challenges and how they relate to ensemble learning (big data streams, concept evolution, feature drifts, temporal dependencies, and others).},
address = {New York, NY, USA},
author = {Gomes, Heitor Murilo and Barddal, Jean Paul and Enembreck, Fabr{\'{i}}cio and Bifet, Albert},


issn = {03600300},
journal = {ACM Computing Surveys},
keywords = {Ensemble learning,data stream classification,supervised learning},
number = {2},
pages = {1--36},
publisher = {ACM},
title = {{A Survey on Ensemble Learning for Data Stream Classification}},

volume = {50},
year = {2017}
}
@article{Fallis2013,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Fallis, A.G},

eprint = {arXiv:1011.1669v3},

isbn = {9788578110796},
issn = {1098-6596},
journal = {Journal of Chemical Information and Modeling},
number = {9},
pages = {1689--1699},
pmid = {25246403},
title = {{Probability {\&} Mathematical Statistics}},
volume = {53},
year = {2013}
}
@article{Qian1999,
abstract = {A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.},
author = {Qian, Ning},


isbn = {1212543521},
issn = {08936080},
journal = {Neural Networks},
keywords = {Critical damping,Damped harmonic oscillator,Gradient descent learning algorithm,Learning rate,Momentum,Speed of convergence},
number = {1},
pages = {145--151},
pmid = {12662723},
title = {{On the momentum term in gradient descent learning algorithms}},
volume = {12},
year = {1999}
}
@article{Li2019a,
abstract = {Domain adaptation aims to leverage knowledge from a well-labeled source domain to a poorly labeled target domain. A majority of existing works transfer the knowledge at either feature level or sample level. Recent studies reveal that both of the paradigms are essentially important, and optimizing one of them can reinforce the other. Inspired by this, we propose a novel approach to jointly exploit feature adaptation with distribution matching and sample adaptation with landmark selection. During the knowledge transfer, we also take the local consistency between the samples into consideration so that the manifold structures of samples can be preserved. At last, we deploy label propagation to predict the categories of new instances. Notably, our approach is suitable for both homogeneous- and heterogeneous-domain adaptations by learning domain-specific projections. Extensive experiments on five open benchmarks, which consist of both standard and large-scale datasets, verify that our approach can significantly outperform not only conventional approaches but also end-to-end deep models. The experiments also demonstrate that we can leverage handcrafted features to promote the accuracy on deep features by heterogeneous adaptation.},
archivePrefix = {arXiv},
arxivId = {1906.07441},
author = {Li, Jingjing and Jing, Mengmeng and Lu, Ke and Zhu, Lei and Shen, Heng Tao},

eprint = {1906.07441},

issn = {1057-7149},
journal = {IEEE Transactions on Image Processing},
keywords = {Domain adaptation,landmark selection,subspace learning,transfer learning},
month = {dec},
number = {12},
pages = {6103--6115},
title = {{Locality Preserving Joint Transfer for Domain Adaptation}},

volume = {28},
year = {2019}
}
@article{Nam2015,
author = {Nam, Jaechang and Kim, Sunghun},


isbn = {9781450336758},
journal = {Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2015},
keywords = {defect prediction,heterogeneous metrics,quality assurance},
pages = {508--519},
title = {{Heterogeneous defect prediction}},

year = {2015}
}
@article{Dai2007a,
abstract = {A basic assumption in traditional machine learning is that the training and test data distributions should be identical. This assumption may not hold in many situations in practice, but we may be forced to rely on a different-distribution data to learn a prediction model. For example, this may be the case when it is expensive to label the data in a domain of interest, although in a related but different domain there may be plenty of labeled data available. In this paper, we propose a novel transfer-learning algorithm for text classification based on an EM-based Naive Bayes classifiers. Our solution is to first estimate the initial probabilities under a distribution D of one labeled data set, and then use an EM algorithm to revise the model for a different distribution Du of the test data which are unlabeled. We show that our algorithm is very effective in several different pairs of domains, where the distances between the different distributions are measured using the Kullback- Leibler (KL) divergence. Moreover, KL-divergence is used to decide the trade-off parameters in our algorithm. In the experiment, our algorithm outperforms the traditional supervised and semi-supervised learning algorithms when the distributions of the training and test sets are increasingly different.},
author = {Dai, Wenyuan and Xue, Gr and Yang, Q and Yu, Y},


isbn = {9781577353232},
issn = {00301299},
journal = {AAAI conference on Artificial Intelligence},
keywords = {Machine Learning,Technical Papers,Transfer Learning},
pages = {540--545},
pmid = {87882338},
title = {{Transferring naive bayes classifiers for text classification}},

year = {2007}
}
@article{Kierzmann2008,
abstract = {We present a new variant of generalized relevance learning vector quantization (GRLVQ) in a computer vision scenario. A version with incrementally added prototypes is used for the non-trivial case of high-dimensional object recognition. Training is based upon a generic set of standard visual features, the learned input weights are used for iterative feature pruning. Thus, prototypes and input space are altered simultaneously, leading to very sparse and task-specific representations. The effectiveness of the approach and the combination of the incremental variant together with pruning was tested on the COIL100 database. It exhibits excellent performance with regard to codebook size, feature selection and recognition accuracy. {\textcopyright} 2007 Elsevier B.V. All rights reserved.},
author = {Kierzmann, Tim C. and Lange, Sascha and Riedmiller, Martin},


isbn = {0925-2312},
issn = {09252312},
journal = {Neurocomputing},
keywords = {Adaptive metric,Feature selection,Incremental learning vector quantization,Object recognition,Relevance learning},
number = {13-15},
pages = {2868--2879},
title = {{Incremental GRLVQ: Learning relevant features for 3D object recognition}},
volume = {71},
year = {2008}
}
@article{Zhao2020,
abstract = {The transfer learning method is used to extend our existing model to more difficult scenarios, thereby accelerating the training process and improving learning performance. The conditional adversarial domain adaptation method proposed in 2018 is a particular type of transfer learning. It uses the domain discriminator to identify which images the extracted features belong to. The features are obtained from the feature extraction network. The stability of the domain discriminator directly affects the classification accuracy. Here, we propose a new algorithm to improve the predictive accuracy. First, we introduce the Lipschitz constraint condition into domain adaptation. If the constraint condition can be satisfied, the method will be stable. Second, we analyze how to make the gradient satisfy the condition, thereby deducing the modified gradient via the spectrum regularization method. The modified gradient is then used to update the parameter matrix. The proposed method is compared to the ResNet-50, deep adaptation network, domain adversarial neural network, joint adaptation network, and conditional domain adversarial network methods using the datasets that are found in Office-31, ImageCLEF-DA, and Office-Home. The simulations demonstrate that the proposed method has a better performance than other methods with respect to accuracy.},
author = {Zhao, Liquan and Liu, Yan},


issn = {2078-2489},
journal = {Information},
keywords = {Adversarial network,Deep learning,Domain adaptation,Transfer learning},
month = {jan},
number = {2},
title = {{Spectral Normalization for Domain Adaptation}},

volume = {11},
year = {2020}
}
@article{Boutsidis2015,
abstract = {We study the Principal Component Analysis (PCA) problem in the distributed and streaming models of computation. Given a matrix {\$}A \backslashin R{\^{}}{\{}m \backslashtimes n{\}},{\$} a rank parameter {\$}k {\textless} rank(A){\$}, and an accuracy parameter {\$}0 {\textless} \backslashepsilon {\textless} 1{\$}, we want to output an {\$}m \backslashtimes k{\$} orthonormal matrix {\$}U{\$} for which {\$}{\$} || A - U U{\^{}}T A ||{\_}F{\^{}}2 $\backslash$le $\backslash$left(1 + $\backslash$epsilon $\backslash$right) $\backslash$cdot || A - A{\_}k||{\_}F{\^{}}2, {\$}{\$} where {\$}A{\_}k \backslashin R{\^{}}{\{}m \backslashtimes n{\}}{\$} is the best rank-{\$}k{\$} approximation to {\$}A{\$}. This paper provides improved algorithms for distributed PCA and streaming PCA.},
archivePrefix = {arXiv},
arxivId = {1504.06729},
author = {Boutsidis, Christos and Woodruff, David P. and Zhong, Peilin},

eprint = {1504.06729},

isbn = {9781450341325},
issn = {07378017},
journal = {Proceedings of the 48th Annual {\{}ACM{\}} {\{}SIGACT{\}} Symposium on Theory of Computing, {\{}STOC{\}}},
keywords = {column subset selection,distributed,low rank matrix decomposition,lower bounds,principal component analysis,singular value decomposi-,sparse,streaming,tion},
pages = {236--249},
title = {{Optimal principal component analysis in distributed and streaming models}},

year = {2016}
}
@article{McGee2012,
abstract = {Blended or hybrid course offerings in higher education are commonplace and much has been written about how to design a blended course effectively. This study examines publically available guides, documents, and books that espouse best or effective practices in blended course design to determine commonalities among such practices. A qualitative meta-analysis reveals common principles regarding the design process, pedagogical strategies, classroom and online technology utilization, assessment strategies, and course implementation and student readiness. Findings reveal areas of disconnect and conflict, as well as implications for the likelihood of successful utilization when best/effective practices are followed.},
author = {Mcgee, Patricia and Reis, Abby},

issn = {ISSN-1939-5256},
journal = {Journal of Asynchronous Learning Networks},
keywords = {best practices,blended,course design,hybrid,pedagogy},
number = {4},
pages = {7--22},
title = {{blended McGee}},
volume = {16},
year = {2012}
}
@article{Wang2019b,
abstract = {Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this paper, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and setup a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance which leads to better performance. We believe this observation can be helpful for future research in transfer learning.},
archivePrefix = {arXiv},
arxivId = {1909.08531},
author = {Wang, Jindong and Chen, Yiqiang and Feng, Wenjie and Yu, Han and Huang, Meiyu and Yang, Qiang},

eprint = {1909.08531},

journal = {Corr},
month = {sep},
title = {{Transfer Learning with Dynamic Distribution Adaptation}},

year = {2019}
}
@inproceedings{Chen_2019_CVPR,
author = {Chen, Ziliang and Zhuang, Jingyu and Liang, Xiaodan and Lin, Liang},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{Blending-Target Domain Adaptation by Adversarial Meta-Adaptation Networks}},
year = {2019}
}
@article{Kierzkowski2011,
abstract = {The block analogues of the theorems on inclusion regions for the eigenvalues of normal matrices are given. By an inclusion region for a given matrix A we mean a region of the complex plane that contains at least one of the eigenvalues of A. Some nonsingularity results for partitioned matrices are also presented.},
author = {Kierzkowski, Jakub and Smoktunowicz, Alicja},

issn = {10813810},
journal = {Electronic Journal of Linear Algebra},
keywords = {Block matrices,Eigenvalues,Gershgorin's discs,Hermitian positive definite matrices,Nonsingular matrices,Normal matrices,Strong square-sum criterion},
number = {October},
pages = {1059--1069},
title = {{Block normal matrices and gershgorin-type discs}},
volume = {22},
year = {2011}
}
@inproceedings{Koniusz_2017_CVPR,
author = {Koniusz, Piotr and Tas, Yusuf and Porikli, Fatih},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors}},
year = {2017}
}
@article{8703078,
abstract = {A representation is supposed universal if it encodes any element of the visual world (e.g., objects, scenes) in any configuration (e.g., scale, context). While not expecting pure universal representations, the goal in the literature is to improve the universality level, starting from a representation with a certain level. To do so, the state-of-the-art consists in learning CNN-based representations on a diversified training problem (e.g., ImageNet modified by adding annotated data). While it effectively increases universality, such approach still requires a large amount of efforts to satisfy the needs in annotated data. In this work, we propose two methods to improve universality, but pay special attention to limit the need of annotated data. We also propose a unified framework of the methods based on the diversifying of the training problem. Finally, to better match Atkinson's cognitive study about universal human representations, we proposed to rely on the transfer-learning scheme as well as a new metric to evaluate universality. This latter, aims us to demonstrates the interest of our methods on 10 target-problems, relating to the classification task and a variety of visual domains.},
author = {Tamaazousti, Youssef and {Le Borgne}, Herve and Hudelot, Celine and Seddik, Mohamed El Amine and Tamaazousti, Mohamed},


issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Additives,Measurement,Semantics,Task analysis,Training,Transfer-Learning,Universal Representations,Universality Evaluation,Veins,Visual Recognition,Visualization},
pages = {1--1},
title = {{Learning More Universal Representations for Transfer-Learning}},

year = {2019}
}
@article{Straat2018,
abstract = {We introduce a modeling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e., the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.},
author = {Straat, Michiel and Abadi, Fthi and G{\"{o}}pfert, Christina and Hammer, Barbara and Biehl, Michael},


issn = {1099-4300},
journal = {Entropy},
keywords = {Development economics,Development strategy,Economic complexity,Economic fitness,Industrial diversification,Public policy},
month = {oct},
number = {10},
pages = {775},
title = {{Statistical Mechanics of On-Line Learning Under Concept Drift}},

volume = {20},
year = {2018}
}
@inproceedings{sun2016return,
author = {Sun, Baochen and Feng, Jiashi and Saenko, Kate},
booktitle = {Proceedings of the Thirtieth {\{}AAAI{\}} Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, {\{}USA.{\}}},

pages = {2058--2065},
title = {{Return of Frustratingly Easy Domain Adaptation}},

year = {2016}
}
@article{Aggarwal2007,
abstract = {In recent years, data streams have become ubiquitous because of the large number of applications which generate huge volumes of data in an automated way. Many existing data mining methods cannot be applied directly on data streams because of the fact that the data needs to be mined in one pass. Furthermore, data streams show a considerable amount of temporal locality because of which a direct application of the existing methods may lead to misleading results. In this paper, we develop an efficient and effective approach for mining fast evolving data streams, which integrates themicro-clusteringtechnique with the high-level data mining process, and discovers data evolution regularities as well. Our analysis and experiments demonstrate two important data mining problems, namelystream clusteringandstream classification, can be performed effectively using this approach, with high quality mining results. We discuss the use of micro-clustering as a general summarization technology to solve data mining problems on streams. Our discussion illustrates the importance of our approach for a variety of mining problems in the data stream domain.},
archivePrefix = {arXiv},
arxivId = {arXiv:1310.8004v1},
author = {Gama, Jo{\~{a}}o and Rodrigues, Pedro Pereira},

eprint = {arXiv:1310.8004v1},

isbn = {9780387287591},
journal = {Encyclopedia of Data Warehousing and Mining, Second Edition},
pages = {561--565},
title = {{Data Streams}},

volume = {31},
year = {2011}
}
@incollection{NIPS2018_7585,
author = {Oliver, Avital and Odena, Augustus and Raffel, Colin A and Cubuk, Ekin Dogus and Goodfellow, Ian},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R},

pages = {3235--3246},
publisher = {Curran Associates, Inc.},
title = {{Realistic Evaluation of Deep Semi-Supervised Learning Algorithms}},

year = {2018}
}
@article{Ma2017,
abstract = {Classification can be highly challenging when the dataset is extremely large, or when the training data in the underlying domain are difficult to obtain. One feasible solution to this challenge is transfer learning, which extracts the knowledge from source tasks and applies the knowledge to target tasks. Extant transfer learning schemes typically assume that similarities between the source task and the target task to some degree. This assumption does not hold in certain actual applications; analysts unfamiliar with the learning strategy can be frustrated by the complicated transfer relations and the non-intuitive transfer process. This paper presents a suite of visual communication and interaction techniques to support the transfer learning process. Furthermore, a pioneering visual-assisted transfer learning methodology is proposed in the context of classification. Our solution includes a visual communication interface that allows for comprehensive exploration of the entire knowledge transfer process and the relevance among tasks. With these techniques and the methodology, the analysts can intuitively choose relevant tasks and data, as well as iteratively incorporate their experience and expertise into the analysis process. We demonstrate the validity and efficiency of our visual design and the analysis approach with examples of text classification.},
author = {Ma, Yuxin and Xu, Jiayi and Wu, Xiangyang and Wang, Fei and Chen, Wei},


issn = {00200255},
journal = {Information Sciences},
keywords = {Classification,Transfer learning,Visual analytics,Visualization},
pages = {54--69},
publisher = {Elsevier Inc.},
title = {{A visual analytical approach for transfer learning in classification}},

volume = {390},
year = {2017}
}
@article{Chandra2016,
abstract = {A typical data stream classification involves predicting label of data instances generated from a non-stationary process. Studies in the past decade have focused on this problem setting to address various challenges such as concept drift and concept evolution. Most techniques assume availability of class labels associated with unlabeled data instances, soon after label prediction, for further training and drift detection. Moreover, training and test data distributions are assumed to be similar. These assumptions are not always true in practice. For instance, a semi-supervised setting that aims to utilize only a fraction of labels may induce bias during data selection. Consequently, the resulting data distribution of training and test instances may differ. In this paper, we present a novel stream classification problem setting involving two independent non-stationary data generating processes, relaxing the above assumptions. A source stream continuously generates labeled data instances whose distribution is biased compared to that of a target stream which generates unlabeled data instances from the same domain. The problem, we call Multistream Classification, is to predict the class labels of data instances in the target stream, while utilizing labels available on the source stream. Since concept drift can occur asynchronously on these two streams, we design an adaptive framework that uses a technique for supervised concept drift detection in the biased source stream, and unsupervised concept drift detection in the target stream. A weighted ensemble of classifiers is updated after each drift detection on either streams, while utilizing a bias correction mechanism that leverage source information to predict labels of target instances whenever necessary. We empirically evaluate the multistream classifier's performance on both real-world and synthetic datasets, while comparing with various baseline methods and its variants.},
author = {Chandra, Swarup and Haque, Ahsanul and Khan, Latifur and Aggarwal, Charu},


isbn = {9781450340731},
journal = {International Conference on Information and Knowledge Management, Proceedings},
keywords = {Classification,Concept drift,Covariate shift,Data stream},
pages = {1181--1190},
title = {{An adaptive framework for multistream classification}},
volume = {24-28-Octo},
year = {2016}
}
@book{breiman1984,
abstract = {The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.},
author = {Moisen, G.G.},
booktitle = {Encyclopedia of Ecology},

isbn = {1420089641},
issn = {13119109},
number = {November},
pages = {582--588},
pmid = {462029},
publisher = {Elsevier},
title = {{Classification and Regression Trees}},

volume = {p},
year = {2008}
}
@article{Cabral2018,
abstract = {Concept drift detectors are software that usually attempt to estimate the positions of concept drifts in large data streams in order to replace the base learner after changes in the data distribution and thus improve accuracy. Statistical Test of Equal Proportions (STEPD) is a simple, efficient, and well-known method which detects concept drifts based on a hypothesis test between two proportions. However, statistically, this test is not recommended when sample sizes are small or data are sparse and/or imbalanced. This article proposes an ingeniously efficient implementation of the statistically preferred but computationally expensive Fisher's Exact test and examines three slightly different applications of this test for concept drift detection, proposing FPDD, FSDD, and FTDD. Experiments run using four artificial dataset generators, with both abrupt and gradual drift versions, as well as three real-world datasets, suggest that the new methods improve the accuracy results and the detections of STEPD and other well-known and/or recent concept drift detectors in many scenarios, with little impact on memory and run-time usage.},
author = {Cabral, Danilo Rafael de Lima and de Barros, Roberto Souto Maior},


issn = {00200255},
journal = {Information Sciences},
keywords = {Concept drift,Data streams,Drift detection,Online learning,Statistical tests},
pages = {220--234},
publisher = {Elsevier Inc.},
title = {{Concept drift detection based on Fisher's Exact test}},

volume = {442-443},
year = {2018}
}
@article{Young2018,
abstract = {Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.},
author = {Young, Tom and Hazarika, Devamanyu and Poria, Soujanya and Cambria, Erik},


issn = {1556-603X},
journal = {IEEE Computational Intelligence Magazine},
keywords = {ek laboratories,nanyang technological university},
month = {aug},
number = {3},
pages = {55--75},
publisher = {IEEE},
title = {{Recent Trends in Deep Learning Based Natural Language Processing [Review Article]}},

volume = {13},
year = {2018}
}
@article{Wu2017a,
abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57{\%} error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28{\%} relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC {\&} COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
archivePrefix = {arXiv},
arxivId = {1512.03385},
author = {Wu, Songtao and Zhong, Shenghua and Liu, Yan},

eprint = {1512.03385},

isbn = {978-1-4673-6964-0},
issn = {15737721},
journal = {Multimedia Tools and Applications},
keywords = {Convolutional neural networks,Image steganalysis,Residual learning},
pages = {1--17},
pmid = {23554596},
title = {{Deep residual learning for image steganalysis}},
year = {2017}
}
@article{Tzeng2017,
abstract = {Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain. While ADDA has already achieved better training efficiency and competitive accuracy in comparison to other adversarial based methods, we investigate whether we can improve performance by incorporating task knowledge into the adversarial loss functions. We achieve this by extending the discriminator output over the source classes and leverage on the distribution over the source encoder posteriors, which is fixed during adversarial training, in order to align a shared encoder distribution to the source domain. The shared encoder can receive a proportion of examples from both the source and target datasets, in order to smooth the learned distribution and improve its convergence properties during adversarial training. We additionally consider how the extended discriminator can be regularized in order to further improve performance, by treating the discriminator as a denoising autoencoder and corrupting its input. Our final design employs maximum mean discrepancy and reconstruction-based loss functions for adversarial training. We validate our framework on standard datasets like MNIST, USPS, SVHN, MNIST-M and Office-31. Our results on all datasets show that our proposal is both simple and efficient, as it competes or outperforms the state-of-the-art in unsupervised domain adaptation, whilst offering lower complexity than other recent adversarial methods such as DIFA and CoGAN.},
annote = {2},
author = {Tzeng, Eric and Hoffman, Judy and Saenko, Kate and Darrell, Trevor},


isbn = {9781538604571},
journal = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
pages = {2962--2971},
title = {{Adversarial discriminative domain adaptation}},
volume = {2017-Janua},
year = {2017}
}
@inproceedings{DBLP:conf/ijcnlp/ZhangW17,
author = {Zhang, Ye and Wallace, Byron C},
booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing, {\{}IJCNLP{\}} 2017, Taipei, Taiwan, November 27 - December 1, 2017 - Volume 1: Long Papers},

pages = {253--263},
title = {{A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification}},

year = {2017}
}
@article{Tzeng2014,
annote = {Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available.

Optimizing for domain invariance, therefore, can be considered equivalent to the task of learning to predict the class labels while si- multaneously finding a representation that makes the domains appear as similar as possible.

Directly training a classifier using only the source data often leads to overfitting to the source distribution, causing reduced performance at test time when recognizing in the target domain. Our intuition is that if we can learn a rep- resentation that minimizes the distance between the source and target distributions, then we can train a classifier on the source labeled data and directly apply it to the target domain with minimal loss in accuracy.


After fine-tuning this architecture, owing to the two terms in the joint loss, the adaptation layer learns a represen- tation that can effectively discriminate between the classes in question due to the classification loss term, while still re- maining invariant to domain shift due the MMD term. We expect that such a representation will thus enable increased adaptation performance.

The Office [29] dataset is a collection of images from
three distinct domains: Amazon, DSLR, and Webcam. The 31 categories in the dataset consist of objects commonly en- countered in office settings, such as keyboards, file cabinets, and laptops. The largest domain has 2817 labeled images. We evaluate our method across 5 random train/test
splits for each of the 3 transfer tasks commonly used for evaluation (Amazon→Webcam, DSLR→Webcam, and Webcam→DSLR) and report averages and standard errors for each setting. We compare in both supervised and un- supervised scenarios against the numbers reported by six recently published methods.

Setting $\lambda$ too low will cause the MMD regularizer have no effect on the learned representa- tion, but setting $\lambda$ too high will regularize too heavily and learn a degenerate representation in which all points are too close together.},
archivePrefix = {arXiv},
arxivId = {1412.3474},
author = {Tzeng, Eric and Hoffman, Judy and Zhang, Ning and Saenko, Kate and Darrell, Trevor},
eprint = {1412.3474},

journal = {CoRR},
title = {{Deep Domain Confusion: Maximizing for Domain Invariance}},

volume = {abs/1412.3},
year = {2014}
}
@article{Zliobaite.2014,
abstract = {Many supervised learning approaches that adapt to changes in data distribution over time (e.g., concept drift) have been developed. The majority of them assume that the data comes already preprocessed or that preprocessing is an integral part of a learning algorithm. In real-application tasks, data that comes from, e.g., sensor readings, is typically noisy, contain missing values, redundant features, and a very large part of model development efforts is devoted to data preprocessing. As data is evolving over time, learning models need to be able to adapt to changes automatically. From a practical perspective, automating a predictor makes little sense if preprocessing requires manual adjustment over time. Nevertheless, adaptation of preprocessing has been largely overlooked in research. In this paper, we introduce and address the problem of adaptive preprocessing. We analyze when and under what circumstances it is beneficial to handle adaptivity of preprocessing and adaptivity of the learning model separately. We present three scenarios where handling adaptive preprocessing separately benefits the final prediction accuracy and illustrate them using computational examples. As a result of our analysis, we construct a prototype approach for combining adaptive preprocessing with adaptive predictor online. Our case study with real sensory data from a production process demonstrates that decoupling the adaptivity of preprocessing and the predictor contributes to improving the prediction accuracy. The developed reference framework and our experimental findings are intended to serve as a starting point in systematic research of adaptive preprocessing mechanisms for adaptive learning with evolving data. {\textcopyright} 1989-2012 IEEE.},
author = {{\v{Z}}liobaite, Indre and Gabrys, Bogdan},


issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Concept drift,adaptive preprocessing,streaming data},
number = {2},
pages = {309--321},
title = {{Adaptive preprocessing for streaming data}},

volume = {26},
year = {2014}
}
@article{DBLP:journals/corr/HannunCCCDEPSSCN14,
archivePrefix = {arXiv},
arxivId = {1412.5567},
author = {Hannun, Awni Y and Case, Carl and Casper, Jared and Catanzaro, Bryan and Diamos, Greg and Elsen, Erich and Prenger, Ryan and Satheesh, Sanjeev and Sengupta, Shubho and Coates, Adam and Ng, Andrew Y},
eprint = {1412.5567},

journal = {CoRR},
title = {{Deep Speech: Scaling up end-to-end speech recognition}},

volume = {abs/1412.5},
year = {2014}
}
@incollection{NIPS2019_9179,
author = {Hanneke, Steve and Kpotufe, Samory},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d'Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {9867--9877},
publisher = {Curran Associates, Inc.},
title = {{On the Value of Target Data in Transfer Learning}},

year = {2019}
}
@article{Rosenstein2005,
abstract = {With transfer learning, one set of tasks is used to bias learning and im- prove performance on another task. However, transfer learning may actually hinder performance if the tasks are too dissimilar. As described in this paper, one challenge for transfer learning research is to develop approaches that detect and avoid negative transfer using very little data from the target task.},
author = {Rosenstein, Michael T and Marx, Zvika and Kaelbling, Leslie Pack and Science, Computer and Dietterich, Thomas G},

journal = {NIPS Workshop on Inductive Transfer},
pages = {1--4},
title = {{To Transfer or Not To Transfer}},
year = {2005}
}
@article{Moraglio2010,
abstract = {Geometric differential evolution (GDE) is a recently introduced formal generalization of traditional differential evolution (DE) that can be used to derive specific differential evolution algorithms for both continuous and combinatorial spaces retaining the same geometric interpretation of the dynamics of the DE search across representations. In this article, we first review the theory behind the GDE algorithm, then, we use this framework to formally derive specific GDE for search spaces associated with binary strings, permutations, vectors of permutations and genetic programs. The resulting algorithms are representation-specific differential evolution algorithms searching the target spaces by acting directly on their underlying representations. We present experimental results for each of the new algorithms on a number of well-known problems comprising NK-landscapes, TSP, and Sudoku, for binary strings, permutations, and vectors of permutations. We also present results for the regression, artificial ant, parity, and multiplexer problems within the genetic programming domain. Experiments show that overall the new DE algorithms are competitive with well-tuned standard search algorithms.},
archivePrefix = {arXiv},
arxivId = {arXiv:1401.1942v1},
author = {Moraglio, Alberto and Togelius, Julian and Silva, Sara},

eprint = {arXiv:1401.1942v1},

isbn = {1063-6560},
issn = {1530-9304},
journal = {Evolutionary Computation (MIT)},
keywords = {QA 76 Software, computer programming,},
number = {4},
pages = {591--624},
pmid = {23270388},
title = {{Geometric Differential Evolution for Combinatorial and Programs Spaces}},

volume = {21},
year = {2010}
}
@article{Krawczyk2018,
abstract = {Most of data stream classifier learning methods assume that a true class of an incoming object is available right after the instance has been processed and new and labeled instance may be used to update a classifier's model, drift detection or capturing novel concepts. However, assumption that we have an unlimited and infinite access to class labels is very naive and usually would require a very high labeling cost. Therefore the applicability of many supervised techniques is limited in real-life stream analytics scenarios. Active learning emerges as a potential solution to this problem, concentrating on selecting only the most valuable instances and learning an accurate predictive model with as few labeling queries as possible. However learning from data streams differ from online learning as distribution of examples may change over time. Therefore, an active learning strategy must be able to handle concept drift and quickly adapt to evolving nature of data. In this paper we present novel active learning strategies that are designed for effective tackling of such changes. We assume that most labeling effort is required when concept drift occurs, as we need a representative sample of new concept to retrain properly the predictive model. Therefore, we propose active learning strategies that are guided by drift detection module to save budget for difficult and evolving instances. Three proposed strategies are based on learner uncertainty, dynamic allocation of budget over time and search space randomization. Experimental evaluation of the proposed methods prove their usefulness for reducing labeling effort in learning from drifting data streams.},
author = {Krawczyk, Bartosz and Pfahringer, Bernhard and Wozniak, Michal},


isbn = {9781538650356},
journal = {Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018},
keywords = {active learning,concept drift,data stream mining,drift detection,machine learning},
pages = {2239--2244},
publisher = {IEEE},
title = {{Combining active learning with concept drift detection for data stream mining}},

year = {2019}
}
@inproceedings{Saralajew2017,
abstract = {The paper deals with realizations of transfer learning for classification, i. e. the adaptation of a classifier model to a changed data distribution. This change could be a data drift or a more complex transformation. We propose to model those data changes by manifolds describing continuous transformations of the data. This description can be seen as a generalization of function based transfer models considered so far. The manifold description of the transfer function allows either to adjust the classifier model to the changed data distribution or a back-transformation of those data to the original data space. To get the approach feasible, the manifold is approximated by the affine part of the Taylor expansion of the manifold structure. Moreover, the affine approximation shows mathematical correspondences to tangent metric leaning, which was developed for handling of data with drifts in classification methods. The paper provides the mathematical background for manifold based transfer data learning. Further, the approach is exemplarily applied for the generalized learning vector quantization classifier. This classifier is a prominent method which frequently achieves a high performance and a robust behavior while the classifier complexity is low compared to more sophisticated approaches like deep learning architectures or support vector machines. Moreover, the good interpretability of learning vector quantization classifiers also contributes to an intuitive practical understanding of transfer learning.},
author = {Saralajew, Sascha and Villmann, Thomas},
booktitle = {2017 International Joint Conference on Neural Networks (IJCNN)},


isbn = {978-1-5090-6182-2},
month = {may},
pages = {1756--1765},
publisher = {IEEE},
title = {{Transfer learning in classification based on manifolc. models and its relation to tangent metric learning}},

volume = {2017-May},
year = {2017}
}
@article{Ye2007,
abstract = {We propose a sliding-window approach for the dimen- sionality reduction for linear discriminant analysis(LDA) on streaming data. Streaming data are time variant and can be in high dimensions. When a sliding window is mov- ing along data stream, the data that have passed out ofthe window will be forgotten (i.e., deleted).We propose a LDA dimensionality reduction algorithm based on different slid- ing windows. The experiments on UCI data sets have been conducted and results are compared with the batch IDR/QR LDA method. It is shown that our algorithm present an effi- cient solution to the problem ofdimensionality reduction on streaming data yet still have a good performance on com- putational cost and the classification accuracy.},
author = {Ye, Mao and Li, Xue and Orlowska, Maria E.},


isbn = {0769528740},
journal = {Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007},
pages = {674--678},
title = {{Supervised dimensionality reduction on streaming data}},
volume = {1},
year = {2007}
}
@article{Pang.2005,
abstract = {This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number of classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.},
author = {Pang, Shaoning and Ozawa, Seiichi and Kasabov, Nikola},


issn = {10834419},
journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics},
keywords = {Classification,Data stream,Incremental linear discriminant analysis,Incremental principle component analysis,Linear discriminant analysis,Pattern recognition,Principle component analysis},
number = {5},
pages = {905--914},
title = {{Incremental linear discriminant analysis for classification of data streams}},

volume = {35},
year = {2005}
}
@article{Wang2017,
abstract = {Text classification is a fundamental task in NLP applications. Most existing work relied on either explicit or implicit text representation to address this problem. While these techniques work well for sentences, they can not easily be applied to short text because of its shortness and sparsity. In this paper, we propose a framework based on convolutional neural networks that combines explicit and implicit representations of short text for classification. We first conceptualize a short text as a set of relevant concepts using a large taxonomy knowledge base. We then obtain the embedding of short text by coalescing the words and relevant concepts on top of pre-trained word vectors. We further incorporate character level features into our model to capture fine-grained subword information. Experimental results on five commonly used datasets show that our proposed method significantly outperforms state-of-the-art methods.},
author = {Wang, Jin and Wang, Zhongyuan and Zhang, Dawei and Yan, Jun},

isbn = {9780999241103},
issn = {10450823},
journal = {IJCAI International Joint Conference on Artificial Intelligence},
keywords = {Machine Learning: Deep Learning,Natural Language Processing: Text Classification},
pages = {2915--2921},
title = {{Combining knowledge with deep convolutional neural networks for short text classification}},
year = {2017}
}
@book{ben2003,
author = {Ben-Israel, Adi and Greville, Thomas N E},
publisher = {Springer Science $\backslash${\&} Business Media},
title = {{Generalized inverses: theory and applications}},
volume = {15},
year = {2003}
}
@inproceedings{DBLP:conf/iclr/LiWS0H17,
author = {Li, Yanghao and Wang, Naiyan and Shi, Jianping and Liu, Jiaying and Hou, Xiaodi},
booktitle = {5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings},

title = {{Revisiting Batch Normalization For Practical Domain Adaptation}},

year = {2017}
}
@article{Muthukrishnan2011,
abstract = {1 S. Muthukrishnan. Data Streams: Algorithms and Applications. In Foundations and Trends in Theoretical Computer Science, 2005. 2 P. Indyk A tutorial on Streaming, Sketching and Sub-linear Space Algorithms. 2009 Information Theory and Applications Workshop, San Diego, 2009. http://people.csail.mit.edu/indyk/ita-web.pdf},
author = {Muthukrishnan, S.},


isbn = {9781450306607},
journal = {Proceedings of the 30th symposium on Principles of database systems of data - PODS '11},
keywords = {data streams},
pages = {317},
title = {{Theory of data stream computing}},

year = {2011}
}
@article{Singh2017,
abstract = {{\textcopyright} 2018, Springer International Publishing AG. The fourth leading disease in the world today is Diabetes and there are number of challenges to predict and identify the disease. Data mining proposes effective approaches to identify the diabetic patients. This paper proposes clustering and classification of effective diabetes diagnosis based on computational intelligence techniques using PCA with kNN. Diabetes disease data is used to identify feature of clusters. Diabetes disease diagnosis proposes novel distribution function applied to classify each patient. This proposed procedure defines clusters and similarity measure based on classifying with each cluster using computational intelligence techniques. PCA using diabetes disease data for dimensionality reduction. Novel similarity measure is proposed in kNN for classification. Accuracy measures are computed for each patient.},
author = {Singh, Shashi Pal and Kumar, Ajai and Darbari, Hemant and Rastogi, Anshika},


isbn = {978-3-319-63672-6},
journal = {Information and Communication Technology for Intelligent Systems (ICTIS 2017)},
keywords = {data stream {\'{a}} ensemble,method,msmote,neighbor {\'{a}} smote {\'{a}}},
number = {Ictis},
pages = {0--7},
title = {{Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1}},

volume = {83},
year = {2018}
}
@inproceedings{Pratama.2015,
abstract = {{\textcopyright} 2015 IEEE. Extreme Learning Machine (ELM) is an answer to an increasing demand for a low-cost learning algorithm to handle big data applications. Nevertheless, existing ELMs leave four uncharted problems: complexity, uncertainty, concept drifts, curse of dimensionality. To correct these issues, a novel incremental meta-cognitive ELM, namely Evolving Type-2 Extreme Learning Machine (eT2ELM), is proposed. Et2Elm is built upon the three pillars of meta-cognitive learning, namely what-To-learn, how-To-learn, when-To-learn, where the notion of ELM is implemented in the how-To-learn component. On the other hand, eT2ELM is driven by a generalized interval type-2 Fuzzy Neural Network (FNN) as the cognitive constituent, where the interval type-2 multivariate Gaussian function is used in the hidden layer, whereas the nonlinear Chebyshev function is embedded in the output layer. The efficacy of eT2ELM is proven with four data streams possessing various concept drifts, comparisons with prominent classifiers, and statistical tests, where eT2ELM demonstrates the most encouraging learning performances in terms of accuracy and complexity.},
author = {Pratama, Mahardhika and Lu, Jie and Zhang, Guangquan},
booktitle = {Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015},


isbn = {9781479986965},
keywords = {evolving neuro fuzzy system,extreme learning machine,fuzzy neural network,metacognitive learning,sequential learning},
pages = {2792--2797},
title = {{A Novel Meta-Cognitive Extreme Learning Machine to Learning from Data Streams}},

year = {2016}
}
@article{Najafabadi2015,
abstract = {Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.},
author = {Najafabadi, Maryam M. and Villanustre, Flavio and Khoshgoftaar, Taghi M. and Seliya, Naeem and Wald, Randall and Muharemagic, Edin},


isbn = {9783319115382},
issn = {21961115},
journal = {Journal of Big Data},
keywords = {Big data,Deep learning},
number = {1},
pages = {1--21},
title = {{Deep learning applications and challenges in big data analytics}},
volume = {2},
year = {2015}
}
@inbook{Lemaire2015,
abstract = {The last ten years were prolific in the statistical learning and data min- ing field and it is now easy to find learning algorithms which are fast and auto- matic. Historically a strong hypothesiswas that all examples were available or can be loaded into memory so that learning algorithms can use them straight away. But recently new use cases generating lots of data came up as for example: moni- toring of telecommunication network, user modeling in dynamic social network, web mining... The volume of data increases rapidly and it is now necessary to use incremental learning algorithms on data streams. This article presents the main approaches of incremental supervised classification available in the literature. It aims to give basic knowledge to a reader novice in this subject. 1},
address = {Cham},
author = {Lemaire, Vincent and Salperwyck, Christophe and Bondu, Alexis},
booktitle = {Lecture Notes in Business Information Processing},

editor = {Zim{\'{a}}nyi, Esteban and Kutsche, Ralf-Detlef},

isbn = {9783319175508},
issn = {18651348},
pages = {88--125},
publisher = {Springer International Publishing},
title = {{A survey on supervised classification on data streams}},

volume = {205},
year = {2015}
}
@inproceedings{Geilke.2014,
abstract = {Data mining and machine learning algorithms usually operate directly on the data. However, if the data is not available at once or consists of billions of instances, these algorithms easily become infeasible with respect to memory and run-time concerns. As a solution to this problem, we propose a framework, called MiDEO (Mining Density Estimates inferred Online), in which algorithms are designed to operate on a condensed representation of the data. In particular, we propose to use density estimates, which are able to represent billions of instances in a compact form and can be updated when new instances arrive. As an example for an algorithm that operates on density estimates, we consider the task of mining association rules, which we consider as a form of simple statements about the data. The algorithm, called POEt (Pattern mining on Online density esTimates), is evaluated on synthetic and real-world data and is compared to state-of-the-art algorithms.},
author = {Geilke, Michael and Karwath, Andreas and Kramer, Stefan},
booktitle = {DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics},


isbn = {9781479969913},
pages = {297--303},
title = {{A probabilistic condensed representation of data for stream mining}},

year = {2014}
}
@article{Ikonomovska.2011,
abstract = {The problem of real-time extraction of meaningful patterns from time-changing data streams is of increasing importance for the machine learning and data mining communities. Regression in time-changing data streams is a relatively unexplored topic, despite the apparent applications. This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. The algorithm is evaluated extensively in a variety of settings involving artificial and real data. To the best of our knowledge there is no other general purpose algorithm for incremental learning regression/model trees able to perform explicit change detection and informed adaptation. The algorithm performs online and in real-time, observes each example only once at the speed of arrival, and maintains at any-time a ready-to-use model tree. The tree leaves contain linear models induced online from the examples assigned to them, a process with low complexity. The algorithm has mechanisms for drift detection and model adaptation, which enable it to maintain accurate and updated regression models at any time. The drift detection mechanism exploits the structure of the tree in the process of local change detection. As a response to local drift, the algorithm is able to update the tree structure only locally. This approach improves the any-time performance and greatly reduces the costs of adaptation.},
author = {Ikonomovska, Elena and Gama, Jo{\~{a}}o and D{\v{z}}eroski, Sa{\v{s}}o},


issn = {13845810},
journal = {Data Mining and Knowledge Discovery},
keywords = {Concept drift,Incremental algorithms,Model trees,Non-stationary data streams,On-line change detection,On-line learning,Regression trees,Stream data mining},
number = {1},
pages = {128--168},
title = {{Learning model trees from evolving data streams}},

volume = {23},
year = {2011}
}
@inproceedings{10.1007/978-3-030-04182-3_18,
abstract = {Training of deep learning algorithms such as CNN, LSTM, or GRU often requires large amount of data. However in real world applications, the amount of data especially labelled data is limited. To address this challenge, we study Deep Transfer Learning (DTL) in the context of Multitasking Learning (MTL) to extract sharable knowledge from tasks and use it for related tasks. In this paper, we use Minimum Closed Ball (MEB) as a flexible knowledge representation method to map shared domain knowledge from primary task to secondary task in multitasking learning. The experiments provide both analytic and empirical results to show the effectiveness and robustness of the proposed MEB-based deep transfer learning.},
address = {Cham},
author = {Deng, Zhilong and Liu, Fan and Zhao, Jiangjiang and Wei, Qiang and Pang, Shaoning and Leng, Yue},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

editor = {Cheng, Long and Leung, Andrew Chi Sing and Ozawa, Seiichi},

isbn = {9783030041816},
issn = {16113349},
keywords = {Deep transfer learning,Learner-independent multi-task learning,Minimum enclosing ball,Multi-task learning},
pages = {198--207},
publisher = {Springer International Publishing},
title = {{Deep transfer learning via minimum enclosing balls}},
volume = {11303 LNCS},
year = {2018}
}
@article{Bifet2007,
abstract = {We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, instead of being fixed a priori, is recomputed online according to the rate of change observed from the data in the window itself. This delivers the user or programmer from having to guess a time-scale for change. Contrary to many related works, we provide rigorous guarantees of performance, as bounds on the rates of false positives and false negatives. Using ideas from data stream algorithmics, we develop a time- and memory-efficient version of this algorithm, called ADWIN2. We show how to combine ADWIN2 with the Naive Bayes (NB) predictor, in two ways: one, using it to monitor the error rate of the current model and declare when revision is necessary and, two, putting it inside the NB predictor to maintain up-to-date estimations of conditional probabilities in the data. We test our approach using synthetic and real data streams.},
author = {Bifet, Albert and Gavald{\`{a}}, Ricard},


isbn = {9780898716306},
journal = {Proceedings of the 2007 SIAM International Conference on Data Mining},
keywords = {and distribution drift,concept,data streams,na,time-changing data},
pages = {443--448},
title = {{Learning from Time-Changing Data with Adaptive Windowing}},

year = {2013}
}
@article{Amini2016,
abstract = {Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams. However, existing density-based data stream clustering algorithms are not without problem. There is a dramatic decrease in the quality of clustering when there is a range in density of data. In this paper, a new method, called the MuDi-Stream, is developed. It is an online-offline algorithm with four main components. In the online phase, it keeps summary information about evolving multi-density data stream in the form of core mini-clusters. The offline phase generates the final clusters using an adapted density-based clustering algorithm. The grid-based method is used as an outlier buffer to handle both noises and multi-density data and yet is used to reduce the merging time of clustering. The algorithm is evaluated on various synthetic and real-world datasets using different quality metrics and further, scalability results are compared. The experimental results show that the proposed method in this study improves clustering quality in multi-density environments.},
author = {Amini, Amineh and Saboohi, Hadi and Herawan, Tutut and Wah, Teh Ying},


issn = {10958592},
journal = {Journal of Network and Computer Applications},
keywords = {Core mini-clusters,Density grid,Evolving data streams,Multi-density clusters},
pages = {370--385},
publisher = {Elsevier},
title = {{MuDi-Stream: A multi density clustering algorithm for evolving data stream}},

volume = {59},
year = {2016}
}
@inproceedings{8455721,
author = {Yang, K and Wan, W and Lu, J},
booktitle = {2018 International Conference on Audio, Language and Image Processing (ICALIP)},


keywords = {Classification algorithms,Data models,Domain Adaptation,Gaussian Process,Gaussian process classification,Gaussian processes,Homogeneous,Kernel,Machine learning,Support vector machines,Training,Transfer Learning,data mining,distribution data,domain adaptation,homogeneous transfer classification problem,learning (artificial intelligence),machining learning,mathematic model,pattern classification,probability,unlabeled data},
pages = {226--229},
title = {{Domain Adaptation for Gaussian Process Classification}},
year = {2018}
}
@article{Bifet2015,
abstract = {The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models.},
author = {Bifet, Albert and {de Francisci Morales}, Gianmarco and Read, Jesse and Holmes, Geoff and Pfahringer, Bernhard},


isbn = {9781450336642},
journal = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
keywords = {all or part of,classification,classroom use is granted,copies are not made,data streams,evaluation,online learning,or,or distributed,or hard copies of,permission to make digital,this work for personal,without fee provided that},
pages = {59--68},
title = {{Efficient Online Evaluation of Big Data Stream Classifiers}},
year = {2015}
}
@article{Brew2008,
abstract = {A graduate level course in library and information science was revised from face-to-face instruction to a blended learning model utilizing on site, interactive television, and online classes. This study investigates three related questions: (1) Will students provide detailed feedback if given the opportunity to complete an anonymous survey? (2) Will embedding an online survey within the course management software be an effective method of gathering feedback? (3) Will student feedback contain constructive critical information that will prove useful in evaluating and revising the course? An online survey created within the WebCT course management software was administered in 2001, 2003, 2005, and 2007. The total number of responses was 71. Results indicate that students were willing to provide detailed feedback, readily accepted the embedded survey, and provided constructive criticism that proved useful in the evaluation and revision process. The study concludes that this method is effective and may be most appropriate for courses with small enrollments. {\textcopyright} 2008 Elsevier Inc. All rights reserved.},
author = {Brew, Linda S.},


isbn = {10967516},
issn = {10967516},
journal = {Internet and Higher Education},
keywords = {Blended learning,Course evaluation,Online surveys,Student feedback},
number = {2},
pages = {98--105},
pmid = {33921846},
title = {{The role of student feedback in evaluating and revising a blended learning course}},
volume = {11},
year = {2008}
}
@inproceedings{pmlr-v37-long15,
abstract = {Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.},
address = {Lille, France},
archivePrefix = {arXiv},
arxivId = {1502.02791},
author = {Long, Mingsheng and Cao, Yue and Wang, Jianmin and Jordan, Michael I.},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
editor = {Bach, Francis and Blei, David},
eprint = {1502.02791},

pages = {97--105},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Learning Transferable Features with Deep Adaptation Networks}},

volume = {37},
year = {2015}
}
@inproceedings{Meng2020,
abstract = {We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target domains. With NLE method, we distill the knowledge from a powerful source-domain DNN into a dictionary of label embeddings, or l-vectors, one for each senone class. Each l-vector is a representation of the senone-specific output distributions of the source-domain DNN and is learned to minimize the average L2, Kullback-Leibler (KL) or symmetric KL distance to the output vectors with the same label through simple averaging or standard back-propagation. During adaptation, the l-vectors serve as the soft targets to train the target-domain model with cross-entropy loss. Without parallel data constraint as in the teacher-student learning, NLE is specially suited for the situation where the paired target-domain data cannot be simulated from the source-domain data. We adapt a 6400 hours multi-conditional US English acoustic model to each of the 9 accented English (80 to 830 hours) and kids' speech (80 hours). NLE achieves up to 14.1{\%} relative word error rate reduction over direct re-training with one-hot labels.},
archivePrefix = {arXiv},
arxivId = {2004.13480},
author = {Meng, Zhong and Hu, Hu and Li, Jinyu and Liu, Changliang and Huang, Yan and Gong, Yifan and Lee, Chin-Hui},
booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

eprint = {2004.13480},

isbn = {978-1-5090-6631-5},
month = {may},
pages = {7389--7393},
publisher = {IEEE},
title = {{L-Vector: Neural Label Embedding for Domain Adaptation}},

year = {2020}
}
@incollection{NIPS2018_7436,
author = {Long, Mingsheng and CAO, ZHANGJIE and Wang, Jianmin and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R},

pages = {1640--1650},
publisher = {Curran Associates, Inc.},
title = {{Conditional Adversarial Domain Adaptation}},

year = {2018}
}
@inproceedings{Lyon,
abstract = {The domain of radio astronomy is currently facing significant computational challenges, foremost amongst which are those posed by the development of the world's largest radio telescope, the Square Kilometre Array (SKA). Preliminary specifications for this instrument suggest that the final design will incorporate between 2000 and 3000 individual 15 metre receiving dishes, which together can be expected to produce a data rate of many TB/s. Given such a high data rate, it becomes crucial to consider how this information will be processed and stored to maximise its scientific utility. In this paper, we consider one possible data processing scenario for the SKA, for the purposes of an all-sky pulsar survey. In particular we treat the selection of promising signals from the SKA processing pipeline as a data stream classification problem. We consider the feasibility of classifying signals that arrive via an unlabelled and heavily class imbalanced data stream, using currently available algorithms and frameworks. Our results indicate that existing stream learners exhibit unacceptably low recall on real astronomical data when used in standard configuration; however, good false positive performance and comparable accuracy to static learners, suggests they have definite potential as an on-line solution to this particular big data challenge.},
author = {Lyon, R. J. and Brooke, J. M. and Knowles, J. D. and Stappers, B. W.},
booktitle = {Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013},


isbn = {9780769551548},
keywords = {Astroinformatics,Classification,Data streams,Imbalanced learning,Unlabelled data},
pages = {1506--1511},
title = {{A study on classification in imbalanced and partially-labelled data streams}},

year = {2013}
}
@article{Marquez2018,
abstract = {In this paper, we present a novel strategy for evolving prototype based clusters that uses a weighting scheme to “progressively forget” old samples. The rate of forgetfulness can be controlled by a single intuitive memory parameter. This weighting scheme can be used to create efficient dynamic summaries, such as mean or covariance, of data streams. Using this weighting scheme we have developed evolving versions of the K-means and Gaussian Mixture models algorithms. They can analyze the incoming data in an online manner and they are specially geared towards dealing with concept drift originated by changes in the underlying data distribution. The algorithms were validated over a simulated database where a wide variety of concept drift situations occur and over real data related to property sales, showing their capability to follow changes in data.},
author = {M{\'{a}}rquez, David G. and Otero, Abraham and F{\'{e}}lix, Paulo and Garc{\'{i}}a, Constantino A.},


isbn = {978-84-9768-666-2},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Cluster evolution,Concept drift,Data stream,Evolving clustering,Gaussian mixture models,K-means},
pages = {16--30},
title = {{A novel and simple strategy for evolving prototype based clustering}},
volume = {82},
year = {2018}
}
@inproceedings{Hu.2010,
abstract = {An effective clustering algorithm called PWStream for probabilistic data stream over sliding window is developed in this paper. The algorithm uses exponential histogram of cluster feature to store the summary information of the most recently arrived tuples, and outdated information is deleted within a certain guaranteed range of error. For the uncertain tuples in data stream, the concepts of strong cluster, transitional cluster and weak cluster are proposed in the PWStream. With these concepts, an effective strategy of choosing cluster based on distance and existence probability is designed, which can find more strong clusters. Theoretical analysis and comprehensive experimental results demonstrate that the proposed method is of high quality and fast processing rate.},
author = {Hu, Wei Cheng and Cheng, Zhuan Liu},
booktitle = {2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010},


isbn = {9781424465262},
keywords = {Clustering,Histogram,Probabilistic data stream,Sliding window},
pages = {2065--2070},
title = {{Clustering algorithm for probabilistic data stream over sliding windows}},

volume = {4},
year = {2010}
}
@article{ZELLINGER2019174,
abstract = {A novel approach for unsupervised domain adaptation for neural networks is proposed. It relies on metric-based regularization of the learning process. The metric-based regularization aims at domain-invariant latent feature representations by means of maximizing the similarity between domain-specific activation distributions. The proposed metric results from modifying an integral probability metric such that it becomes less translation-sensitive on a polynomial function space. The metric has an intuitive interpretation in the dual space as the sum of differences of higher order central moments of the corresponding activation distributions. Under appropriate assumptions on the input distributions, error minimization is proven for the continuous case. As demonstrated by an analysis of standard benchmark experiments for sentiment analysis, object recognition and digit recognition, the outlined approach is robust regarding parameter changes and achieves higher classification accuracies than comparable approaches.},
author = {Zellinger, Werner and Moser, Bernhard A and Grubinger, Thomas and Lughofer, Edwin and Natschl{\"{a}}ger, Thomas and Saminger-Platz, Susanne},


issn = {0020-0255},
journal = {Information Sciences},
keywords = {Domain adaptation,Integral probability metric,Moment distance,Neural networks,Transfer learning},
pages = {174--191},
title = {{Robust unsupervised domain adaptation for neural networks via moment alignment}},

volume = {483},
year = {2019}
}
@article{Bruff2013,
abstract = {Although massive open online courses (MOOCs) are seen to be, and are in fact designed to be, stand-alone online courses, their introduction to the higher education landscape has expanded the space of possibilities for blended course designs (those that combine online and face-to-face learning experiences). Instead of replacing courses at higher education institutions, could MOOCs enhance those courses? This paper reports one such exploration, in which a Stanford University Machine Learning MOOC was integrated into a graduate course in machine learning at Vanderbilt University during the Fall 2012 semester. The blended course design, which leveraged a MOOC course and platform for lecturing, grading, and discussion, enabled the Vanderbilt instructor to lead an overload course in a topic much desired by students. The study shows that while students regarded some elements of the course positively, they had concerns about the coupling of online and in-class components of this particular blended course design. Analysis of student and instructor reflections on the course suggests dimensions for characterizing blended course designs that incorporate MOOCs, either in whole or in part. Given the reported challenges in this case study of integrating a MOOC in its entirety in an on-campus course, the paper advocates for more complex forms of blended learning in which course materials are drawn from multiple MOOCs, as well as from other online sources.},
author = {Bruff, Derek O and Fisher, Douglas H and McEwen, Kathryn E and Smith, Blaine E},

isbn = {15589528},
issn = {1558-9528},
journal = {MERLOT Journal of Online Learning and Teaching},
keywords = {blended learning,course cohesion,course customization,flipped classroom,global learning communities,local learning communities,massive open online course (MOOC),online learning,subject coupling,task coupling,wrapper},
number = {2},
pages = {187--199},
title = {{Wrapping a MOOC: Student Perceptions of an Experiment in Blended Learning}},
volume = {9},
year = {2013}
}
@article{Morshed2018,
abstract = {{\textcopyright} 2014 IEEE. Emerging availability (and varying complexity and types) of Internet of Things (IoT) devices, along with large data volumes that such devices (can potentially) generate, can have a significant impact on our lives, fuelling the development of critical next-generation services and applications in a variety of application domains (e.g. healthcare, smart grids, finance, disaster management, agriculture, transportation and water management). Deep learning technology, which has in the past been used successfully in computer vision and language modelling is now finding application in new domains driven by availability of diverse and large datasets. One such example is the advances in medical diagnostics and prediction by using Deep Learning technologies to improve human health. However, transferring large data streams (a requirement of Deep Learning technologies for achieving high accuracy) to centralised locations such as Cloud datacentre environments, in a timely and reliable manner, is being seen as a key limitation of expanding the application horizons of such technologies. To this end, various paradigms, including Osmotic Computing, have been proposed that promotes distribution of data analysis tasks across Cloud and Edge computing environments. However, these existing paradigms fail to provide a detailed account of how technologies such as deep learning can be orchestrated and take advantage of the cloud, edge and mobile edge environments in a holistic manner. In other words, the focus of this Blue Skies piece is to analyze the research challenges involved with developing a new class of holistic distributed deep learning algorithms that are 'resource and data aware', and which are able to account for underlying heterogeneous data and data models, resource (cloud vs. edge vs. mobile edge) models and data availability while executing-trading accuracy for execution time, etc.},
author = {Morshed, Ahsan and Jayaraman, Prem Prakash and Sellis, Timos and Georgakopoulos, Dimitrios and Villari, Massimo and Ranjan, Rajiv},


issn = {23256095},
journal = {IEEE Cloud Computing},
keywords = {cloud IoT,deep learning,distributed,holistic,osmosis},
number = {6},
pages = {22--32},
title = {{Deep Osmosis: Holistic Distributed Deep Learning in Osmotic Computing}},
volume = {4},
year = {2018}
}
@inproceedings{Wang.2014,
abstract = {{\textcopyright} 2014 IEEE.The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification. We conduct an extensive set of experiments, in which encouraging results validate the efficacy of the proposed algorithms in comparison to a family of state-of-the-art techniques on a variety of data stream classification tasks.},
author = {Wang, Dayong and Wu, Pengcheng and Zhao, Peilin and Wu, Yue and Miao, Chunyan and Hoi, Steven C.H.},
booktitle = {Proceedings - IEEE International Conference on Data Mining, ICDM},


issn = {15504786},
keywords = {data stream classification,online learning,sparse},
number = {January},
pages = {1007--1012},
title = {{High-Dimensional Data Stream Classification via Sparse Online Learning}},

volume = {2015-Janua},
year = {2015}
}
@article{Villmann2017,
abstract = {Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems.Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques.This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve.According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles.Thus, the intention of the paper is to provide a comprehensive overview of the stateof- the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.},
author = {Villmann, Thomas and Bohnsack, Andrea and Kaden, Marika},


issn = {24496499},
journal = {Journal of Artificial Intelligence and Soft Computing Research},
keywords = {Classification learning,Prototype based learning,Vector quantization},
number = {1},
pages = {65--81},
title = {{Can learning vector quantization be an alternative to SVM and deep learning? - Recent trends and advanced variants of learning vector quantization for classification learning}},
volume = {7},
year = {2017}
}
@article{Gui2018,
abstract = {Transfer learning method has been widely used in machine learning when training data is limited. However, class noise accumulated during learning iterations can lead to negative transfer which can adversely affect performance when more training data is used. In this paper, we propose a novel method to identify noise samples for noise reduction. More importantly, the method can detect the point where negative transfer happens such that transfer learning can terminate at the near top performance point. In this method, we use the sum of the Rademacher distribution to estimate the class noise rate of transferred data. Transferred data having high probability of being labeled wrongly is removed to reduce noise accumulation. This negative sample reduction process can be repeated several times during transfer learning until we find the point where negative transfer occurs. As we can detect the point where negative transfer occurs, our method not only has the ability to delay the point where negative transfer happens, but also the ability to stop transfer learning algorithms at the right place for top performance gain. Evaluation based on cross-lingual/domain opinion analysis evaluation data set shows that our algorithm achieves the state-of-the-art result. Furthermore, our system shows a monotonic increase trend in performance improvement when more training data are used beating the performance degradation curse of most transfer learning methods when training data reaches certain size.},
author = {Gui, Lin and Xu, Ruifeng and Lu, Qin and Du, Jiachen and Zhou, Yu},


isbn = {0123456789},
issn = {1868808X},
journal = {International Journal of Machine Learning and Cybernetics},
keywords = {Class noise detection,Negative transfer,Transfer learning},
number = {2},
pages = {185--197},
publisher = {Springer Berlin Heidelberg},
title = {{Negative transfer detection in transductive transfer learning}},
volume = {9},
year = {2018}
}
@article{Loosli2016,
abstract = {{\textcopyright} 2015 IEEE.This paper presents a theoretical foundation for an SVM solver in Kreǐn spaces. Up to now, all methods are based either on the matrix correction, or on non-convex minimization, or on feature-space embedding. Here we justify and evaluate a solution that uses the original (indefinite) similarity measure, in the original Kreǐn space. This solution is the result of a stabilization procedure. We establish the correspondence between the stabilization problem (which has to be solved) and a classical SVM based on minimization (which is easy to solve). We provide simple equations to go from one to the other (in both directions). This link between stabilization and minimization problems is the key to obtain a solution in the original Kreǐn space. Using KSVM, one can solve SVM with usually troublesome kernels (large negative eigenvalues or large numbers of negative eigenvalues). We show experiments showing that our algorithm KSVM outperforms all previously proposed approaches to deal with indefinite matrices in SVM-like kernel methods.},
author = {Loosli, Ga{\"{e}}lle and Canu, St{\'{e}}phane and Ong, Cheng Soon},


issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Krein spaces,SVM,classification,dissimilarity,indefinite kernel,stabilization problem},
number = {6},
pages = {1204--1216},
title = {{Learning SVM in Kreǐn Spaces}},
volume = {38},
year = {2016}
}
@inproceedings{3f27e85748124342b07b44b38cea884e,
abstract = {This paper addresses the general problem of domain adaptation which arises in a variety of applications where the distribution of the labeled sample available somewhat differs from that of the test data. Building on previous work by Ben-David et al. (2007), we introduce a novel distance between distributions, discrepancy distance, that is tailored to adaptation problems with arbitrary loss functions. We give Rademacher complexity bounds for estimating the discrepancy distance from finite samples for different loss functions. Using this distance, we derive new generalization bounds for domain adaptation for a wide family of loss functions. We also present a series of novel adaptation bounds for large classes of regularization-based algorithms, including support vector machines and kernel ridge regression based on the empirical discrepancy. This motivates our analysis of the problem of minimizing the empirical discrepancy for various loss functions for which we also give several algorithms. We report the results of preliminary experiments that demonstrate the benefits of our discrepancy minimization algorithms for domain adaptation.},
author = {Mansour, Yishay and Mohri, Mehryar and Rostamizadeh, Afshin},
booktitle = {COLT 2009 - The 22nd Conference on Learning Theory},

title = {{Domain adaptation: Learning bounds and algorithms}},
year = {2009}
}
@article{SANTOS2019376,
abstract = {Predicting online on data streams, with data flowing continuously, quickly, and in large quantities, is becoming increasingly more important in tackling real-world problems. In such scenarios, data distribution usually evolves over time, a situation known as concept drift. This article presents an empirical method, based on a differential evolution, aimed at guiding users on how to tune concept drift detectors to improve their accuracies in data streams. It also suggests the best detectors, strategies and/or parameterization to use in different data stream scenarios. The most time-consuming part of the method was preprocessed and is based on experiments using eight artificial dataset generators, each of them with five abrupt, fast gradual, and slow gradual concept drift versions (120 dataset versions in all), as well as six real-world datasets, for 11 different drift detectors and two different base learners. The use of the proposed method is illustrated mostly with Drift Detection Method (DDM) together with Naive Bayes, but we compared the performances of all 11 detectors using their default parameter values and the several parameter sets prescribed by the method using both base classifiers—Naive Bayes and Hoeffding Trees. Results indicate that the predictive accuracies of the detectors tuned using the method increased considerably in many situations.},
author = {Santos, Silas G.T.C. and Barros, Roberto S.M. and Gon{\c{c}}alves, Paulo M.},


issn = {00200255},
journal = {Information Sciences},
keywords = {Concept drift detectors,Data stream,Differential evolution,Online learning,Tuning},
pages = {376--393},
title = {{A differential evolution based method for tuning concept drift detectors in data streams}},

volume = {485},
year = {2019}
}
@incollection{NIPS2019_8466,
annote = {Catastropic Forgetting: Tendency of model to lose previous learnt knowledge abruptly while it adopts new knowledge to the target classification task. This leads to overfitting

Negative Transfer: Not all trained knowledge is transferable accross domains. ={\textgreater} Negative Transfer is lowered with sufficient labeled target data.
Indisciminate transfer of all knowledge is harmful to the model.

Methods to prevent catastropic forgetting:
L2: Standard Regularization aka. Weight decay drawing weights closer to zero
L2-SP: Tries to drive weight parameters closer to inital pre-trained weights
(Supervised therefore not relevant) Delta: Aligns model behaviour of specific higher layer of the target network to the source one.

Transferability of Subspaces:
Features are transferable if the the angle of the two eigenvectors of the weight vecotrs are similar
={\textgreater} cosinus similariy is 1
Transferavbility of Weights: Eigenvectors of lower layers of the pretrained and fine tuned network have small angles ={\textgreater} parameters are transferable.
For weight parameters, almost all eigenvectors in lower layers are transferable, while in higher layers only eigenvectors with large singular values are transferable.

Batch Spectral Shrinage (BSS): Square the smallest K of the genearted features.
={\textgreater}
Truncated SVD: Dont calculate the smallest r singular values ={\textgreater} low-Rank approximation
Large r with a large labeld traget dataset is harming the accuracy

Implementation:
Not on top of the network but after the flatten layer.
Code: github.com/thuml/Batch-Spectral-Shrinkage

Experiments:
Last Layer of Network is trained from scratch
Different percentage of sampling rates= 15,30,50,100
Resnet59

Parameters:
LR last layer * 10
Momentum 0.95
LR others: 0.01 or 0.001
Batch Size:48
Trade-Off Parameters = 0.001 k = 1
5 TImes repetition

Fine-Tune Experiments:
Stanford Dogs
FGVC Aircraft

The General Language Understanding Evaluation (GLUE) benchmark [39] is a collection of diverse natural language understanding tasks.
Batch Size 32
Epochs 3
Setup as in J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In},
author = {Chen, Xinyang and Wang, Sinan and Fu, Bo and Long, Mingsheng and Wang, Jianmin},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d'Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {1908--1918},
publisher = {Curran Associates, Inc.},
title = {{Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning}},

year = {2019}
}
@inproceedings{DB15a,
author = {Springenberg, J T and Dosovitskiy, A and Brox, T and Riedmiller, M},
booktitle = {ICLR (workshop track)},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Springenberg et al.{\_}2015{\_}ICLR (workshop track).pdf:pdf},
mendeley-groups = {Paper/ASN},
title = {{Striving for Simplicity: The All Convolutional Net}},

year = {2015}
}

@misc{Petersen2008,
abstract = {Matrix identities, relations and approximations. A desktop reference for quick overview of mathematics of matrices.},
annote = {Version 20081110},
author = {Petersen, K B and Pedersen, M S},
keywords = {matrixderivative inverse Matrixidentity matrixrelations},
publisher = {Technical University of Denmark},
title = {{The Matrix Cookbook}},

year = {2008}
}


@article{friedman1937,
author = {Firedman, Milton},
issn = {01621459},
journal = {Journal of the American Statistical Association},
number = {200},
pages = {675--701},
publisher = {[American Statistical Association, Taylor {\&} Francis, Ltd.]},
title = {{The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance}},

volume = {32},
year = {1937}
}
@incollection{Soetens2002,
abstract = {Minoan peak sanctuaries are located on or close to specific mountain peaks dispersed over the Cretan mountains. Their use as Bronze Age sanctuaries covers a period from ± 2300 B.C. to 1500 B.C., which corresponds more or less to the two main building phases of the ‘palaces' (proto- and neopalatial periods). Previous research has interpreted these sites as sanctuaries, based mainly on its finds. Further spatial characteristics, such as distance to the settlements, intervisibility with other sanctuaries and settlements have been explained mostly in terms of this sanctity. A suggestion was made that the intervisible peak sanctuaries were reflection of “zones” [1], and “on a regional level the intervisibility of peak sanctuaries provides an opportunity for the expression of ritual unity may have transcended political boundaries” [2]. The purposes of this project are to investigate these observations systematically, to identify further functionalities of the peak sanctuaries, to better understand how the whole landscape was perceived by the Minoans and all of this by the creation of models capable of spatial, statistical analysis and prediction. GIS was used to organize, analyze and visualize the combined information layers of archaeological, topographical, environmental, and statistical data.},
address = {Cham},
author = {Wen, Yandong and Zhang, Kaipeng and Li, Zhifeng and Qiao, Yu},
booktitle = {European Space Agency, (Special Publication) ESA SP},

editor = {Leibe, Bastian and Matas, Jiri and Sebe, Nicu and Welling, Max},

isbn = {978-3-319-46477-0},
issn = {03796566},
keywords = {center loss,convolutional neural networks,discrim-,face recognition,inative feature learning},
number = {515},
pages = {499--515},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {{A Discriminative Feature Learning Approach for Deep Face Recognition}},

volume = {9911},
year = {2016}
}
@article{Baena-Garcia2006,
abstract = {An emerging problem in Data Streams is the detection of concept drift. This problem is aggravated when the drift is gradual over time. In this work we define a method for detecting concept drift, even in the case of slow gradual change. It is based on the estimated distribution of the distances between classification errors. The proposed method can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental and online algorithm. The experimentation results compare our method (EDDM) with a similar one (DDM). Latter uses the error-rate instead of distance-error-rate},
author = {Baena-Garc{\'{i}}a, Manuel and del Campo-{\'{A}}vila, Jos{\'{e}} and Fidalgo, Ra{\'{u}}l and Bifet, Albert and Gavald{\`{a}}, Ricard and Morales-Bueno, Rafael},


isbn = {3-540-23237-0},
issn = {0302-9743},
journal = {4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams},
keywords = {computational,information theoretic learning with statistics,learning,statistics {\&} optimisation},
pages = {77--86},
title = {{Early Drift Detection Method}},

volume = {6},
year = {2006}
}
@incollection{NIPS2012_4684,
author = {Zhang, Chao and Zhang, Lei and Ye, Jieping},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {Pereira, F and Burges, C J C and Bottou, L and Weinberger, K Q},

pages = {3320--3328},
publisher = {Curran Associates, Inc.},
title = {{Generalization Bounds for Domain Adaptation}},

year = {2012}
}
@article{ZhouShuangSmirnovEvgueniSchienmakersGijsPeetersRalf,
author = {{Zhou, Shuang, Smirnov, Evgueni, Schienmakers, Gijs, Peeters, Ralf}, Jiang Tao},

journal = {Proceedings of Machine Learning Research 91},
title = {{Conformal Feature-Selection Wrappers for Instance Transfer}},
year = {2018}
}
@article{Medhat.2005,
abstract = {The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.},
author = {Gaber, Mohamed Medhat and Zaslavsky, Arkady and Krishnaswamy, Shonali},


issn = {0163-5808},
journal = {SIGMOD Record},
keywords = {computer science,data mining,data science,data set,data stream,data stream mining,database,software,streams,wireless sensor network},
number = {2},
pages = {18--26},
title = {{Mining data streams: A review}},

volume = {34},
year = {2005}
}
@inproceedings{WenyuanDai.2007,
abstract = {In many real world applications, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data from a related but different domain. Traditional machine learning is not able to cope well with learning across different domains. In this paper, we address this problem for a text-mining task, where the labeled data are under one distribution in one domain known as in-domain data, while the unlabeled data are under a related but different domain known as out-of-domain data. Our general goal is to learn from the in-domain and apply the learned knowledge to out-of-domain. We propose a co-clustering based classification (CoCC) algorithm to tackle this problem. Co-clustering is used as a bridge to propagate the class structure and knowledge from the in-domain to the out-of-domain. We present theoretical and empirical analysis to show that our algorithm is able to produce high quality classification results, even when the distributions between the two data are different. The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms.},
author = {Dai, Wenyuan and Xue, Gui-Rong and Yang, Qiang and Yu, Yong},
booktitle = {Proceedings of the 13th {\{}ACM{\}} {\{}SIGKDD{\}} International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15, 2007},


pages = {210},
title = {{Co-clustering based classification for out-of-domain documents}},
year = {2007}
}
@article{B2018,
author = {Gabdulkhakova, Aysylu and Langer, Maximilian and Langer, Bernhard W. and Kropatsch, Walter G.},


isbn = {9783319977843},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Confocal ellipses,Hausdorff distance,Line Voronoi diagram},
pages = {258--267},
title = {{Line voronoi diagrams using elliptical distances}},
volume = {11004 LNCS},
year = {2018}
}
@article{Attar.2010,
abstract = {Analysis of data streams is becoming a key area of data mining research, as the number of applications demanding such processing increases. Modern information technology allows information to be collected at a far greater rate than ever before. Machine learning offers promise of a solution, but the field mainly focuses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set size. When the data set size gets to a point where it could be considered to represent a continuous supply or data stream then incremental classification algorithms are required. When tackling with non-stationary concepts, ensemble of classifiers has several advantages over single classifier methods. They are easy to scale and parallelize, they can adapt to change quickly by pruning underperforming parts of the ensemble and they therefore usually generate more accurate concept descriptions and efficient results. But the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model. This paper proposes a fast and light classifier for data stream classification. {\textcopyright} Springer-Verlag 2010.},
author = {Attar, Vahida and Sinha, Pradeep and Wankhade, Kapil},


issn = {18686478},
journal = {Evolving Systems},
keywords = {Boosting,Fast and light,Hoeffding tree,Sliding window,Stream data mining},
number = {3},
pages = {199--207},
title = {{A fast and light classifier for data streams}},

volume = {1},
year = {2010}
}
@inproceedings{Anh.2016,
abstract = {{\textcopyright} 2016 IEEE. Sentiment analysis of online users has been attracting significant interests in both academics and industry, but is always challenging. In this paper, we propose an effective algorithm that can work with text streams and big text collections, without human supervision. This method is based on the state-of-the-art model, namely Aspect and Sentiment Unification Model (ASUM). Our method has several advantages compared to existing approaches: (i) it is a streaming algorithm which is able to deal with large or streaming data, with whom existing approaches cannot work; (ii) at the core of our algorithm is an inference method which has a theoretical guarantee about quality and convergence rate, whereas existing methods do not have; (iii) by further proposing to keep prior knowledge when learning, our method can do sentiment classification better. Extensive experiments on different review datasets show the effectiveness and efficiency of our algorithm in practice.},
author = {{Le Anh}, Vu and {Van Phung}, Chien and Cao, Cuong Vu and {Van Ngo}, Linh and Than, Khoat},
booktitle = {2016 IEEE RIVF International Conference on Computing and Communication Technologies: Research, Innovation, and Vision for the Future, RIVF 2016 - Proceedings},


isbn = {9781509041336},
month = {nov},
pages = {181--186},
publisher = {IEEE},
title = {{Streaming aspect-sentiment analysis}},

year = {2016}
}
@book{Bishop2013,
abstract = {Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein−protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-$\alpha$-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM- GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 {\AA} for the interface backbone atoms) increased from 21{\%} with default Glide SP settings to 58{\%} with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63{\%} success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40{\%} of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Neal, Radford M},
booktitle = {Technometrics},

eprint = {arXiv:1011.1669v3},

isbn = {978-0-387-31073-2},
issn = {0040-1706},
number = {3},
pages = {366--366},
pmid = {25246403},
title = {{Pattern Recognition and Machine Learning}},

volume = {49},
year = {2007}
}
@article{Lakshmi2015,
author = {Lakshmi, K. Prasanna and Reddy, C. R.K.},


isbn = {9781467365376},
journal = {2015 5th International Conference on IT Convergence and Security, ICITCS 2015 - Proceedings},
keywords = {Accuracy,Algorithm design and analysis,Association rules,Classification algorithms,Data models,Prediction algorithms},
title = {{Fast rule-based prediction of data streams using associative classification mining}},
year = {2015}
}
@article{Jingjing2020,
abstract = {Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named Adversarial Tight Match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named Maximum Density Divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations. Codes and datasets used in this paper are available at {\{}$\backslash$it github.com/lijin118/ATM{\}}.},
archivePrefix = {arXiv},
arxivId = {2004.12615},
author = {Li, Jingjing and Chen, Erpeng and Ding, Zhengming and Zhu, Lei and Lu, Ke and Shen, Heng Tao},

eprint = {2004.12615},

issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {c},
pages = {1--1},
title = {{Maximum Density Divergence for Domain Adaptation}},

volume = {8828},
year = {2020}
}
@article{Raina2006,
abstract = {Many applications of supervised learning re- quire good generalization from limited la- beled data. In the Bayesian setting, we can try to achieve this goal by using an informa- tive prior over the parameters, one that en- codes useful domain knowledge. Focusing on logistic regression, we present an algorithm for automatically constructing a multivariate Gaussian prior with a full covariance matrix for a given supervised learning task. This prior relaxes a commonly used but overly simplistic independence assumption, and al- lows parameters to be dependent. The algo- rithm uses other “similar” learning problems to estimate the covariance of pairs of indi- vidual parameters. We then use a semidefi- nite program to combine these estimates and learn a good prior for the current learning task. We apply our methods to binary text classification, and demonstrate a 20 to 40{\%} test error reduction over a commonly used prior.},
author = {Raina, Rajat and Ng, Andrew Y. and Koller, Daphne},


isbn = {1595933832},
journal = {Proceedings of the 23rd international conference on Machine learning  - ICML '06},
pages = {713--720},
title = {{Constructing informative priors using transfer learning}},

year = {2006}
}
@article{Prahm2017,
abstract = {Restoring human motor and cognitive function has been a fascinating research area during the last century. Interfacing the human nervous system with electro-mechanical rehabilitation machines is facing its crucial passage from research to clinical practice, enhancing the potentiality of therapists, clinicians and researchers to rehabilitate, diagnose and generate knowledge. The 2012 International Conference on Neurorehabilitation (ICNR2012, www.icnr2012.org) brings together researchers and students from the fields of Clinical Rehabilitation, Applied Neurophysiology and Biomedical Engineering, covering a wide range of research topics: {\textperiodcentered} Clinical Impact of Technology{\textperiodcentered} Brain-Computer Interface in Rehabilitation{\textperiodcentered} Neuromotor {\&} Neurosensory modeling and processing{\textperiodcentered} Biomechanics in Rehabilitation{\textperiodcentered} Neural Prostheses in Rehabilitation{\textperiodcentered} Neuro-Robotics in Rehabilitation{\textperiodcentered} Neuromodulation This Proceedings book includes general contributions from oral and poster sessions, as well as from special sessions. A section is also dedicated to pre-post conference workshops, including invited contributions from internationally recognized researchers. A selection of most relevant papers have been considered for publication in international journals (e. g. AJPM{\&}R, JNER, JACCES), therefore they will appear soon in their extended versions in Special Issues.},
author = {Prahm, Cosima and Paassen, Benjamin and Schulz, Alexander and Hammer, Barbara and Aszmann, Oskar},


isbn = {978-3-319-46668-2},
issn = {2195-3562},
journal = {Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016)},
pages = {153--157},
title = {{Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis After Electrode Shift}},

volume = {15},
year = {2017}
}
@inproceedings{pmlr-v70-arjovsky17a,
abstract = {We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to different distances between distributions.},
address = {International Convention Centre, Sydney, Australia},
annote = {Wasserstein Distance for Training GANS:

Classicial generative models (maximum likelihood models) can not approximate distributions with support on low dimensional manifolds. Therefore, it is unlike that the model distribution and the true distribution matches.
={\textgreater} Therefore a noise is added.

GANS are able to learn these manifolds due to the objective functions. But training of GANS is difficult to problems like vanishing gradient or exploding gradient of the Discriminator. The main reason for this is the measure of distance betweeen probability distributions.

Typical distsances like Jensen-Shannon, Kullback-Leibler and Total-Variation suffer from:
Not-continious and dont provide usefull gradients. Saturation of the disriminator is also common.

The wasserstein distance has the proberty to be continues, differentiable almost everywhere and more sensitive as above measures.
The W distance also provides an almost linear gradient so the discriminator can be trained to be optimal.

However, the original wasserstein distance formuluation is computational intractable. By the Kantorovich-Rubinstein duality it is possible to reformulate the wasserstein distance that the difference of means is a valid approximation. This requires f(x) (network) to be k-liptschitz hence the norm of the function ||f|| {\textless}=k.
The solution is to clip the weights of the network to be in the range (-c,c) that the set of weights is closed and compact.},
author = {Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'{e}}on},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
editor = {Precup, Doina and Teh, Yee Whye},

pages = {214--223},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Wasserstein Generative Adversarial Networks}},

volume = {70},
year = {2017}
}
@inproceedings{Phan2020,
author = {Phan, Trung V. and Sultana, Shabnam and Nguyen, Tri Gia and Bauschert, Thomas},
booktitle = {2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)},


isbn = {978-1-7281-4985-1},
month = {feb},
pages = {146--151},
publisher = {IEEE},
title = {{{\$}Q{\$} - TRANSFER: A Novel Framework for Efficient Deep Transfer Learning in Networking}},

year = {2020}
}
@inproceedings{10.1007/978-3-030-19642-4_20,
abstract = {Concept drift is a change of the underlying data distribution which occurs especially with streaming data. Besides other challenges in the field of streaming data classification, concept drift should be addressed to obtain reliable predictions. The Robust Soft Learning Vector Quantization has already shown good performance in traditional settings and is modified in this work to handle streaming data. Further, momentum-based stochastic gradient descent is applied to tackle concept drift passively due to increased learning capabilities. The proposed work is tested against common benchmark algorithms and streaming data in the field.},
address = {Cham},
author = {Heusinger, Moritz and Raab, Christoph and Schleif, Frank Michael},
booktitle = {Advances in Intelligent Systems and Computing},

editor = {Vellido, Alfredo and Gibert, Karina and Angulo, Cecilio and {Mart{\'{i}}n Guerrero}, Jos{\'{e}} David},

isbn = {9783030196417},
issn = {21945357},
keywords = {Concept drift,Robust Soft Learning Vector Quantization,Stream classification},
pages = {200--209},
publisher = {Springer International Publishing},
title = {{Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization}},

volume = {976},
year = {2020}
}
@article{Gretton2012,
abstract = {Given samples from distributions p and q, a two-sample test determines whether to reject the null hypothesis that p = q, based on the value of a test statistic measuring the distance between the samples. One choice of test statistic is the maximum mean discrepancy (MMD), which is a distance between embeddings of the probability distributions in a reproducing kernel Hilbert space. The kernel used in obtaining these embeddings is critical in ensuring the test has high power, and correctly distinguishes unlike distributions with high probability. A means of parameter selection for the two-sample test based on the MMD is proposed. For a given test level (an upper bound on the probability of making a Type I error), the kernel is chosen so as to maximize the test power, and minimize the probability of making a Type II error. The test statistic, test threshold, and optimization over the kernel parameters are obtained with cost linear in the sample size. These properties make the kernel selection and test procedures suited to data streams, where the observations cannot all be stored in memory. In experiments, the new kernel selection approach yields a more powerful test than earlier kernel selection heuristics.},
author = {Gretton, A and Sriperumbudur, B and Sejdinovic, D and Strathmann, H and Balakrishnan, S and Pontil, M and Fukumizu, K},

isbn = {9781627480031},
issn = {1049-5258},
journal = {Advances in Neural Information Processing Systems},
pages = {1205--1213},
title = {{Optimal kernel choice for large-scale two-sample tests}},
volume = {2},
year = {2012}
}
@inproceedings{Su_2020_WACV,
author = {Su, Jong-Chyi and Tsai, Yi-Hsuan and Sohn, Kihyuk and Liu, Buyu and Maji, Subhransu and Chandraker, Manmohan},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},

title = {{Active Adversarial Domain Adaptation}},
year = {2020}
}
@article{Xu2012,
author = {Xu, Ye and Shen, Furao and Zhao, Jinxi},


issn = {09410643},
journal = {Neural Computing and Applications},
keywords = {Adaptive threshold,Classification compression ratio,Incremental learning,Learning vector quantization},
number = {6},
pages = {1205--1215},
title = {{An incremental learning vector quantization algorithm for pattern classification}},
volume = {21},
year = {2012}
}
@inproceedings{Cao.2006,
abstract = {Clustering is an important task in mining evolving data streams. Beside the limited memory and one-pass constraints, the nature of evolving data streams implies the following requirements for stream clustering: no assumption on the number, of clusters, discovery of clusters with arbitrary shape and ability to handle outliers. While a lot of clustering algorithms for data streams have been proposed, they offer no solution to the combination of these requirements. In this paper, we present DenStream, a new approach for discovering clusters in an evolving data stream. The "dense" micro-cluster (named core-micro-cluster) is introduced to summarize the clusters with arbitrary shape, while the potential core-micro-cluster and outlier micro-cluster structures are proposed to maintain and distinguish the potential clusters and outliers. A novel pruning strategy is designed based on these concepts, which guarantees the precision of the weights of the micro-clusters with limited memory. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.},
author = {Cao, Feng and Estert, Martin and Qian, Weining and Zhou, Aoying},
booktitle = {Proceedings of the Sixth {\{}SIAM{\}} International Conference on Data Mining, April 20-22, 2006, Bethesda, MD, {\{}USA{\}}},


pages = {328--339},
title = {{Density-Based Clustering over an Evolving Data Stream with Noise}},

year = {2006}
}
@inproceedings{10.1007/978-3-319-62410-5_15,
abstract = {{\textcopyright} Springer International Publishing AG 2018. The growth in the amount of multimedia content available online supposes a challenge for search and recommender systems. This information in the form of visual elements is of great value to a variety of web mining tasks; however, the mining of these resources is a difficult task due to the complexity and variability of the images. In this paper, we propose applying a deep learning model to the problem of web categorization. In addition, we make use of a technique known as transfer or inductive learning to drastically reduce the computational cost of the training phase. Finally, we report experimental results on the effectiveness of the proposed method using different classification methods and features from various depths of the deep model.},
address = {Cham},
author = {L{\'{o}}pez-S{\'{a}}nchez, Daniel and Arrieta, Ang{\'{e}}lica Gonz{\'{a}}lez and Corchado, Juan M.},
booktitle = {Advances in Intelligent Systems and Computing},

editor = {Omatu, Sigeru and Rodr{\'{i}}guez, Sara and Villarrubia, Gabriel and Faria, Pedro and {Sitek Pawe{\l}and Prieto}, Javier},

isbn = {9783319624099},
issn = {21945357},
keywords = {Deep learning,Transfer learning,Web mining},
pages = {124--131},
publisher = {Springer International Publishing},
title = {{Deep neural networks and transfer learning applied to multimedia web mining}},
volume = {620},
year = {2018}
}
@article{Cruz1994,
author = {Cruz, Santa and Graz, Technische Universitaet},

journal = {Machine Learning},
pages = {27--45},
title = {{Tracking Drifting Concepts By Minimizing Disagreements}},
volume = {45},
year = {1994}
}
@article{Tzeng2015,
abstract = {Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings.},
archivePrefix = {arXiv},
arxivId = {arXiv:1510.02192v1},
author = {Hoffman, Judy and Tzeng, Eric and Darrell, Trevor and Saenko, Kate},

eprint = {arXiv:1510.02192v1},

isbn = {978-1-4673-8391-2},
issn = {21916594},
journal = {Advances in Computer Vision and Pattern Recognition},
number = {9783319583464},
pages = {173--187},
title = {{Simultaneous deep transfer across domains and tasks}},

year = {2017}
}
@article{Alon1996,
author = {Alon, Noga},

isbn = {8979178551961},
title = {{The space complexity}},
year = {1996}
}
@article{Cortes2019,
abstract = {We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm, (DM), previously shown to outperform a number of algorithms for this problem. Unlike many previously proposed solutions for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. Instead, the reweighting depends on the hypothesis sought. The algorithm is derived from a less conservative notion of discrepancy than the DM algorithm called generalized discrepancy. We present a detailed description of our algorithm and show that it can be formulated as a convex optimization problem. We also give a detailed theoretical analysis of its learning guarantees which helps us select its parameters. Finally, we report the results of experiments demonstrating that it improves upon discrepancy minimization.},
author = {Cortes, Corinna and Mohri, Mehryar and Medina, Andr{\'{e}}s Mu{\~{n}}oz},

issn = {15337928},
journal = {Journal of Machine Learning Research},
keywords = {Domain adaptation,Learning theory},
pages = {1--30},
title = {{Adaptation based on generalized discrepancy}},
volume = {20},
year = {2019}
}
@article{Akhtar2018,
abstract = {Deep learning is at the heart of the current rise of machine learning and artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has lead to a large influx of contributions in this direction. This article presents the first comprehensive survey on adversarial attacks on deep learning in Computer Vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, we draw on the literature to provide a broader outlook of the research direction.},
archivePrefix = {arXiv},
arxivId = {1801.00553},
author = {Akhtar, Naveed and Mian, Ajmal},

eprint = {1801.00553},

isbn = {1475-2875 (Electronic)$\backslash$r1475-2875 (Linking)},
issn = {21693536},
journal = {IEEE Access},
keywords = {Deep learning,adversarial learning,adversarial perturbation,black-box attack,perturbation detection,white-box attack},
pages = {14410--14430},
pmid = {25150840},
publisher = {IEEE},
title = {{Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey}},
volume = {6},
year = {2018}
}
@incollection{NIPS2005_2762,
author = {Blanchard, Gilles and Zwald, Laurent},
booktitle = {Advances in Neural Information Processing Systems 18},
editor = {Weiss, Y and Sch{\"{o}}lkopf, B and Platt, J C},

pages = {1649--1656},
publisher = {MIT Press},
title = {{On the Convergence of Eigenspaces in Kernel Principal Component Analysis}},

year = {2006}
}
@inproceedings{DBLP:conf/nips/JinCBJ17,
author = {Jin, Wengong and Coley, Connor W and Barzilay, Regina and Jaakkola, Tommi S},
booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, {\{}USA{\}}},

pages = {2607--2616},
title = {{Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network}},

year = {2017}
}
@inproceedings{Hauberg16,
abstract = {Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original training data; e.g.{\~{}}new images are formed by rotating old ones. Current augmentation schemes, however, rely on manual specification of the applied transformations, making data augmentation an implicit form of feature engineering. With an eye towards true end-to-end learning, we suggest learning the applied transformations on a per-class basis. Particularly, we align image pairs within each class under the assumption that the spatial transformation between images belongs to a large class of diffeomorphisms. We then learn a class-specific probabilistic generative models of the transformations in a Riemannian submanifold of the Lie group of diffeomorphisms. We demonstrate significant performance improvements in training deep neural nets over manually-specified augmentation schemes. Our code and augmented datasets are available online.},
archivePrefix = {arXiv},
arxivId = {1510.02795},
author = {Hauberg, S{\o}ren and Freifeld, Oren and Larsen, Anders Boesen Lindbo and Fisher, John W. and Hansen, Lars Kai},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, {\{}AISTATS{\}} 2016, Cadiz, Spain, May 9-11, 2016},
eprint = {1510.02795},

pages = {342--350},
title = {{Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation}},

year = {2015}
}
@article{Masarotto2019,
abstract = {Covariance operators are fundamental in functional data analysis, providing the canonical means to analyse functional variation via the celebrated Karhunen–Lo{\`{e}}ve expansion. These operators may themselves be subject to variation, for instance in contexts where multiple functional populations are to be compared. Statistical techniques to analyse such variation are intimately linked with the choice of metric on covariance operators, and the intrinsic infinite-dimensionality of these operators. In this paper, we describe the manifold-like geometry of the space of trace-class infinite-dimensional covariance operators and associated key statistical properties, under the recently proposed infinite-dimensional version of the Procrustes metric (Pigoli et al. Biometrika 101, 409–422, 2014). We identify this space with that of centred Gaussian processes equipped with the Wasserstein metric of optimal transportation. The identification allows us to provide a detailed description of those aspects of this manifold-like geometry that are important in terms of statistical inference; to establish key properties of the Fr{\'{e}}chet mean of a random sample of covariances; and to define generative models that are canonical for such metrics and link with the problem of registration of warped functional data.},
archivePrefix = {arXiv},
arxivId = {1801.01990},
author = {Masarotto, Valentina and Panaretos, Victor M. and Zemel, Yoav},

eprint = {1801.01990},

issn = {0976-836X},
journal = {Sankhya A},
keywords = {Fr{\'{e}}chet mean,Functional data analysis,Manifold statistics,Optimal coupling,Tangent space PCA,Trace-class operator},
month = {feb},
number = {1},
pages = {172--213},
publisher = {Sankhya A},
title = {{Procrustes Metrics on Covariance Operators and Optimal Transportation of Gaussian Processes}},

volume = {81},
year = {2019}
}
@article{Mansour2014,
abstract = {We derive a generalization bound for domain adaptation by$\backslash$r$\backslash$nusing the properties of robust algorithms. Our new bound depends$\backslash$r$\backslash$non -shift, a measure of prior knowledge regarding the$\backslash$r$\backslash$nsimilarity of source and target domain distributions. Based on$\backslash$r$\backslash$nthe generalization bound, we design SVM variants for binary$\backslash$r$\backslash$nclassification and regression domain adaptation algorithms.},
author = {Mansour, Yishay and Schain, Mariano},


issn = {1012-2443},
journal = {Annals of Mathematics and Artificial Intelligence},
keywords = {Adaptation,Robustness,SVM},
month = {aug},
number = {4},
pages = {365--380},
title = {{Robust domain adaptation}},

volume = {71},
year = {2014}
}
@article{Karbalayghareh2018a,
author = {Karbalayghareh, Alireza and Qian, Xiaoning and Dougherty, Edward R.},


isbn = {9781538679333},
journal = {2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings},
pages = {1--4},
publisher = {IEEE},
title = {{Optimal Bayesian Transfer Learning for Classifying Multivariate Gaussian Observations}},
year = {2018}
}
@book{Abdi2007,
abstract = {The more tests we perform on a set of data, the more likely we are to reject the null hypothesis when it is true (i.e., a “Type I” error). This is a consequence of the logic of hypothesis testing: We reject the null hypothesis if we witness a rare event. But the larger the number of tests, the easier it is to find rare events and therefore the easier it is to make the mistake of thinking that there is an effect when there is none. This problem is called the inflation of the alpha level. In order to be protected fromit, one strategy is to correct the alpha level when performing multiple tests. Making the alpha level more stringent (i.e., smaller) will create less errors, but itmay alsomake it harder to detect real effects.},
address = {2455 Teller Road, Thousand Oaks California 91320 United States of America},
author = {Salkind, Neil},
booktitle = {Encyclopedia of Measurement and Statistics},


isbn = {9781412916110},
pages = {103--107},
publisher = {Sage Publications, Inc.},
title = {{Encyclopedia of Measurement and Statistics}},

year = {2007}
}
@article{SeoSambuObermayer2003,
author = {Seo, Sambu and Obermayer, Klaus},

journal = {Neural Computation},
pages = {1589--1604},
title = {{Soft Learning Vector Quantization}},
volume = {15},
year = {2002}
}
@article{Silva2013,
abstract = {Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.},
author = {Silva, Jonathan A. and Faria, Elaine R. and Barros, Rodrigo C. and Hruschka, Eduardo R. and de Carvalho, Andr{\'{e}} C. P. L. F. and Gama, Jo{\~{a}}o},


isbn = {0360-0300},
issn = {03600300},
journal = {ACM Computing Surveys},
number = {1},
pages = {1--31},
title = {{Data stream clustering}},

volume = {46},
year = {2013}
}
@article{Jia2008,
abstract = {This paper proposes DD-Stream, a framework for density-based clustering stream data. The algorithm adopts a density decaying technique to capture the evolving data stream and extracts the boundary point of grid by the DCQ-means algorithm. Our method resolving the problem of evolving automatic clustering of real-time data streams, cannot only find arbitrary shaped clusters with noise, but also avoid the clustering quality problems caused by discarding the boundary point of grid, our algorithm has better scalability in processing large-scale and high dimensional stream data as well.},
author = {Jia, Chen and Tan, Chengyu and Yong, Ai},


isbn = {9780769533346},
journal = {Proceedings - 2nd International Conference on Genetic and Evolutionary Computing, WGEC 2008},
pages = {517--521},
title = {{A grid and density-based clustering algorithm for processing data stream}},
year = {2008}
}
@article{DeMello2019,
abstract = {Motivated by the Statistical Learning Theory (SLT), which provides a theoretical framework to ensure when supervised learning algorithms generalize input data, this manuscript relies on the Algorithmic Stability framework to prove learning bounds for the unsupervised concept drift detection on data streams. Based on such proof, we also designed the Plover algorithm to detect drifts using different measure functions, such as Statistical Moments and the Power Spectrum. In this way, the criterion for issuing data changes can also be adapted to better address the target task. From synthetic and real-world scenarios, we observed that each data stream may require a different measure function to identify concept drifts, according to the underlying characteristics of the corresponding application domain. In addition, we discussed about the differences of our approach against others from literature, and showed illustrative results confirming the usefulness of our proposal.},
author = {de Mello, Rodrigo F. and Vaz, Yule and Grossi, Carlos H. and Bifet, Albert},


issn = {09574174},
journal = {Expert Systems with Applications},
keywords = {Algorithmic stability,Concept drift,Data streams,McDiarmid's inequality},
month = {mar},
pages = {90--102},
publisher = {Elsevier Ltd},
title = {{On learning guarantees to unsupervised concept drift detection on data streams}},

volume = {117},
year = {2019}
}
@article{Wang2016a,
abstract = {While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within this framework, two separately developed research areas are bridged together, and a batch of theoretically sound online cost-sensitive bagging and online cost-sensitive boosting algorithms are first proposed. Unlike other online cost-sensitive learning algorithms lacking theoretical analysis of asymptotic properties, the convergence of the proposed algorithms is guaranteed under certain conditions, and the experimental evidence with benchmark data sets also validates the effectiveness and efficiency of the proposed methods},
author = {Wang, Boyu and Pineau, Joelle},


issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Bagging,boosting,class imbalance problem,cost-sensitive learning,ensemble learning,online learning},
number = {12},
pages = {3353--3366},
title = {{Online Bagging and Boosting for Imbalanced Data Streams}},

volume = {28},
year = {2016}
}
@article{Yu2012,
abstract = {In real supervised learning scenarios, it is not uncommon that the training and test sample follow different probability distributions, thus rendering the necessity to correct the sampling bias. Focusing on a particular covariate shift problem, we derive high probability confidence bounds for the kernel mean matching (KMM) estimator, whose convergence rate turns out to depend on some regularity measure of the regression function and also on some capacity measure of the kernel. By comparing KMM with the natural plug-in estimator, we establish the superiority of the former hence provide concrete evidence/understanding to the effectiveness of KMM under covariate shift. Copyright 2012 by the author(s)/owner(s).},
archivePrefix = {arXiv},
arxivId = {1206.4650},
author = {Yu, Yao Liang and Szepesv{\'{a}}ri, Csaba},
eprint = {1206.4650},

isbn = {9781450312851},
journal = {Proceedings of the 29th International Conference on Machine Learning, ICML 2012},
pages = {607--614},
title = {{Analysis of kernel mean matching under covariate shift}},
volume = {1},
year = {2012}
}
@article{Jaber2013,
author = {Jaber, Ghazal},

journal = {Dissertation},
pages = {213},
title = {{An approach for online learning in the presence of concept change}},
year = {2013}
}
@misc{Tieleman2012,
abstract = {Tieleman, Tijmen and Hinton, Geoffrey. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 2012},
author = {Tieleman, Tijmen and Hinton, Geoffrey},
booktitle = {COURSERA: Neural Networks for Machine Learning},


howpublished = {COURSERA: Neural Networks for Machine Learning},
isbn = {9788578110796},
issn = {1098-6596},
title = {{Lecture 06}},

year = {2012}
}
@incollection{NIPS2017_7149,
author = {Mallasto, Anton and Feragen, Aasa},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {Guyon, I and Luxburg, U V and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R},

pages = {5660--5670},
publisher = {Curran Associates, Inc.},
title = {{Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes}},

year = {2017}
}
@article{Li_2019,
abstract = {In biological measuring instruments design, it is time-consuming and expensive to acquire sufficient samples for calibration process. Transfer learning has provided an efficient approach for this problem by leveraging the labeled samples from calibration channels, considered as source domain, to annotate the target domain which has few labels under actual working conditions. In this paper, we design a general transfer regression framework improved by data-augmented ensemble learning called DA-TRTs. First, we modify two-stage TrAdaBoost.R2 to TrAdaBoost. RT (TRT) which concentrates more on hard examples during transfer boosting. Second, model stacking framework is introduced to improve the performance of regression, calling TRT as a base learner. Then, we introduce a simple data augmentation routine, based on label smoothing assumption. Data augmentation improves the strength in diversity of base learners and implicitly controls model complexity. Finally, to illustrate the performance of DA-TRTs, we conduct experiments on synthetic datasets.},
annote = {Uses SMOTE},
author = {Li, Linchun and Ma, Cheng},


issn = {1755-1315},
journal = {IOP Conference Series: Earth and Environmental Science},
month = {jul},
pages = {022095},
publisher = {{\{}IOP{\}} Publishing},
title = {{Transfer Regression with Data-Augmented Ensemble Learning Framework}},

volume = {252},
year = {2019}
}
@article{Iwashita2018,
abstract = {Concept drift techniques aim at learning patterns from data streams that may change over time. Although such behavior is not usually expected in controlled environments, real-world scenarios can face changes in the data, such as new classes, clusters, and features. Traditional classifiers can be easily fooled in such situations, resulting in poor performances. Common concept drift domains include recommendation systems, energy consumption, artificial intelligence systems with dynamic environment interaction, and
biomedical signal analysis (e.g., neurogenerative diseases). In this paper, we surveyed several works that deal with concept drift, as well as we presented a comprehensive study of public synthetic and real datasets that can be used to cope with such a problem. In addition, we considered a review of different types of drifts and approaches to handling such changes in the data. We considered different learners employed in
classification tasks and the use of drift detection mechanisms, among other characteristics.},
author = {Iwashita, Adriana Sayuri and Papa, Jo{\~{a}}o Paulo},


issn = {21693536},
journal = {IEEE Access},
keywords = {Concept drift,machine learning,pattern recognition},
number = {Section III},
pages = {1532--1547},
publisher = {IEEE},
title = {{An Overview on Concept Drift Learning}},

volume = {7},
year = {2019}
}
@inproceedings{Xiao2018,
author = {Xiao, Pan and Du, Bo and Yun, Shuang and Lit, Xue and Zhang, Yipeng and Wu, Jia},
booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)},


isbn = {978-1-5386-3788-3},
issn = {10514651},
month = {aug},
pages = {1283--1288},
publisher = {IEEE},
title = {{Probabilistic Graph Embedding for Unsupervised Domain Adaptation}},

volume = {2018-Augus},
year = {2018}
}
@article{6790375,
abstract = {A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.},
author = {Sch{\"{o}}lkopf, Bernhard and Smola, Alexander and M{\"{u}}ller, Klaus Robert},


issn = {08997667},
journal = {Neural Computation},
number = {5},
pages = {1299--1319},
title = {{Nonlinear Component Analysis as a Kernel Eigenvalue Problem}},
volume = {10},
year = {1998}
}
@article{Grandvalet2002,
abstract = {We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach in-cludes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solu-tion benefits from unlabeled data. The method challenges mixture mod-els when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the " cluster assumption " . Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces.},
author = {Grandvalet, Yves and Bengio, Yoshua},

title = {{Semi-supervised Learning by Entropy Minimization Yves}},

year = {2002}
}
@article{qi1984some,
author = {Qi, Liqun},

journal = {Linear Algebra and Its Applications},
pages = {105--119},
publisher = {Elsevier},
title = {{Some simple estimates for singular values of a matrix}},
volume = {56},
year = {1984}
}
@inproceedings{Gamage.2017,
abstract = {Many real world stochastic processes are non-stationary, which means that the probability distribution that generates data samples is time-varying. In the context of machine learning, this phenomenon is known as concept drift. It is important that machine learning models are able to adapt to concept drift in order to prevent degradation in accuracy. In this paper, we present two algorithms for drift detection and adaptation.},
address = {New York, NY, USA},
author = {Gamage, Sunanda and Premaratne, Upeka},
booktitle = {Proceedings of the International Conference on Big Data and Internet of Thing},


isbn = {978-1-4503-5430-1},
keywords = {concept drift,drift adaptation,drift detection,ensemble methods,incremental learning,machine learning},
pages = {109--114},
publisher = {ACM},
series = {BDIOT2017},
title = {{Detecting and Adapting to Concept Drift in Continually Evolving Stochastic Processes}},

year = {2018}
}
@article{Wandabwa2017,
abstract = {Content comprehension in text is one of the chal- lenges in natural language processing. Understanding text at a low level has become increasingly relevant due to the surge in the amount of content on the web space, where most of it is stream data. In our case, data streams are considered to be an ordered sequence of short and noisy textual messages that are read once or fewer number of times, for example tweets. Our approach entails processing and interpreting streaming texts at document level in mini-batches via deep convolutional networks for opinion,semantic or relationship analysis. Training our model is iterative and incremental, where documents are learnt by understanding the sentence structure and content in vector form based on a known offline model. The model however, incrementally adapts to the changing textual patterns. Our conceptual framework design is distributed in nature such that a pipeline of inputs, deep processing framework and output will be coordinated by the Apache Storm framework. This design is applicable in real time sentiment analysis, opinion mining, and stance detection among others.},
author = {Wandabwa, Herman and Naeem, Muhammad Asif and Mirza, Farhaan},


isbn = {9781509061990},
journal = {Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design, CSCWD 2017},
pages = {475--479},
title = {{Document level semantic comprehension of noisy text streams via convolutional neural networks}},
year = {2017}
}
@inproceedings{You_2019_CVPR,
author = {You, Kaichao and Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{Universal Domain Adaptation}},
year = {2019}
}
@inproceedings{pmlr-v27-bengio12a,
abstract = {Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. The objective is to make these higher- level representations more abstract, with their individual features more invariant to most of the variations that are typically present in the training distribution, while collectively preserving as much as possible of the information in the input. Ideally, we would like these representations to disentangle the unknown factors of variation that underlie the training distribution. Such unsupervised learning of representations can be exploited usefully under the hypothesis that the input distribution P(x) is structurally related to some task of interest, say predicting P(y|x). This paper focusses on why unsupervised pre-training of representations can be useful, and how it can be exploited in the transfer learning scenario, where we care about predictions on examples that are not from the same distribution as the training distribution},
address = {Bellevue, Washington, USA},
archivePrefix = {arXiv},
arxivId = {1305.0445},
author = {Bengio, Yoshua and Courville, Aaron},
booktitle = {JMLR: Workshop and Conference Proceedings 27},

editor = {Guyon, Isabelle and Dror, Gideon and Lemaire, Vincent and Taylor, Graham and Silver, Daniel},
eprint = {1305.0445},

isbn = {9780971977778},
issn = {18684394},
keywords = {auto-encoders,deep learning,domain adaptation,ing,multi-task learning,neural networks,re-,representation learning,self-taught learning,stricted boltzmann machines,transfer learn-,unsupervised learning},
pages = {1--28},
pmid = {21070559},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Deep Learning of Representations for Unsupervised and Transfer Learning}},

volume = {27},
year = {2013}
}
@article{Alphonse1979,
author = {Alphonse, Gerard A.},


issn = {0161-7346},
journal = {Ultrasonic Imaging},
keywords = {Broadband,PZT,electrical matching,piezoelectric tranducers,wedge coupling},
month = {jan},
number = {1},
pages = {76--88},
title = {{The Wedged Transducer — A Transducer Design for Broad Band Characteristics}},

volume = {1},
year = {1979}
}
@article{Ramirez-Gallego2017,
abstract = {Data preprocessing and reduction have become essential techniques in current knowledge discovery scenarios, dominated by increasingly large datasets. These methods aim at reducing the complexity inherent to real-world datasets, so that they can be easily processed by current data mining solutions. Advantages of such approaches include, among others, a faster and more precise learning process, and more understandable structure of raw data. However, in the context of data preprocessing techniques for data streams have a long road ahead of them, despite online learning is growing in importance thanks to the development of Internet and technologies for massive data collection. Throughout this survey, we summarize, categorize and analyze those contributions on data preprocessing that cope with streaming data. This work also takes into account the existing relationships between the different families of methods (feature and instance selection, and discretization). To enrich our study, we conduct thorough experiments using the most relevant contributions and present an analysis of their predictive performance, reduction rates, computational time, and memory usage. Finally, we offer general advices about existing data stream preprocessing algorithms, as well as discuss emerging future challenges to be faced in the domain of data stream preprocessing.},
author = {Ram{\'{i}}rez-Gallego, Sergio and Krawczyk, Bartosz and Garc{\'{i}}a, Salvador and Wo{\'{z}}niak, Micha{\l} and Herrera, Francisco},


isbn = {0925-2312},
issn = {18728286},
journal = {Neurocomputing},
keywords = {Concept drift,Data discretization,Data mining,Data preprocessing,Data reduction,Data stream,Feature selection,Instance selection,Online learning},
pages = {39--57},
title = {{A survey on data preprocessing for data stream mining: Current status and future directions}},
volume = {239},
year = {2017}
}
@article{Zhang2019,
abstract = {The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as domain transfer adaptation when it needs knowledge correspondence between different moments. Conventional machine learning aims to find a model with the minimum expected risk on test data by minimizing the regularized empirical risk on the training data, which, however, supposes that the training and test data share similar joint probability distribution. Transfer adaptation learning aims to build models that can perform tasks of target domain by learning knowledge from a semantic related but distribution different source domain. It is an energetic research filed of increasing influence and importance. This paper surveys the recent advances in transfer adaptation learning methodology and potential benchmarks. Broader challenges being faced by transfer adaptation learning researchers are identified, i.e., instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation, and adversarial adaptation, which are beyond the early semi-supervised and unsupervised split. The survey provides researchers a framework for better understanding and identifying the research status, challenges and future directions of the field.},
archivePrefix = {arXiv},
arxivId = {1903.04687},
author = {Zhang, Lei},
eprint = {1903.04687},

journal = {CoRR},
month = {mar},
number = {March},
pages = {1--21},
title = {{Transfer Adaptation Learning: A Decade Survey}},

year = {2019}
}
@article{Raab2020,
abstract = {The amount of real-time communication between agents in an information system has increased rapidly since the beginning of the decade. This is because the use of these systems, e.g. social media, has become commonplace in today's society. This requires analytical algorithms to learn and predict this stream of information in real-time. The nature of these systems is non-static and can be explained, among other things, by the fast pace of trends. This creates an environment in which algorithms must recognize changes and adapt. Recent work shows vital research in the field, but mainly lack stable performance during model adaptation. In this work, a concept drift detection strategy followed by a prototype-based adaptation strategy is proposed. Validated through experimental results on a variety of typical non-static data, our solution provides stable and quick adjustments in times of change.},
author = {Raab, Christoph and Heusinger, Moritz and Schleif, Frank-Michael},


issn = {09252312},
journal = {Neurocomputing},
keywords = {Concept Drift,Kolmogorov-Smirnov,Momentum-Based Gradient Descent,Prototype Adaptation,RMSprop,Robust Soft Learning Vector Quantization,Stream Classification},
month = {apr},
number = {xxxx},
publisher = {Elsevier B.V.},
title = {{Reactive Soft Prototype Computing for Concept Drift Streams}},

year = {2020}
}
@inproceedings{Gama.04,
abstract = {Most of the work in machine learning assume that exam- ples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generate the examples changes over time.We present a method for detection of changes in the probability distribution of examples. The idea behind the drift detection method is to control the online error-rate of the algorithm. The training examples are presented in sequence. When a new training example is available, it is classified using the actual model. Statistical theory guarantees that while the distribution is stationary, the error will decrease.When the distribution changes, the error will increase. The method controls the trace of the online error of the algorithm. For the actual context we define a warning level, and a drift level. A new context is declared, if in a sequence of examples, the error increases reaching the warning level at example kw,and the drift level at example kd. This is an indication of a change in the distribution of the examples. The algorithm learns a new model using only the examples since kw. The method was tested with a set of eight artificial datasets and a real world dataset. We used three learning algorithms: a perceptron,a neural network and a decision tree. The experimental re- sults show a good performance detecting drift and with learning the new concept.We also observe that the method is independent of the learning algorithm.},
address = {Berlin, Heidelberg},
author = {Gama, Jo{\~{a}}o and Medas, Pedro and Castillo, Gladys and Rodrigues, Pedro},
booktitle = {Advances in Artificial Intelligence -- SBIA 2004},

editor = {Bazzan, Ana L C and Labidi, Sofiane},

isbn = {978-3-540-28645-5},
keywords = {concept drift,incremental supervised learning,machine},
pages = {286--295},
publisher = {Springer Berlin Heidelberg},
title = {{Learning with Drift Detection}},

year = {2010}
}
@article{Zhao2019,
abstract = {Unsupervised domain adaptation (UDA) aims to learn a prediction model for the target domain given labeled source data and unlabeled target data. Impressive progress has been made by adversarial learning-based methods that align distributions across domains through deceiving a domain discriminator network. However, these methods only try to align two domains and neglect the boundaries between classes, which may lead to false alignment and poor generalization performance. In contrast, consistency-enforcing methods exploit the target data posterior distribution to make the target features far away from decision boundaries. Despite their efficacy, these approaches require additional intensity augmentation to align distributions when encountering datasets with large domain discrepancy. To solve the above problems, we propose a novel UDA method that unifies the adversarial learning-based method and consistency-enforcing method together to take both domain alignment and boundaries between classes into consideration. In addition to the supervised classification on the source domain and the adversarial domain adaptation, we introduce interpolation consistency into the UDA task. To be specific, we first construct robust and informative pseudo labels for target samples, and then we encourage the prediction at an interpolation of unlabeled target samples to be consistent with the interpolation of the pseudo labels of these samples. The extensive empirical results demonstrate that our method achieves state-of-the-art results on both digit classification and object recognition tasks.},
author = {Zhao, Xin and Wang, Shengsheng},


issn = {2169-3536},
journal = {IEEE Access},
keywords = {Domain adaptation,deep learning,image classification,transfer learning},
pages = {170448--170456},
publisher = {IEEE},
title = {{Adversarial Learning and Interpolation Consistency for Unsupervised Domain Adaptation}},

volume = {7},
year = {2019}
}
@article{Gisbrecht.2015,
abstract = {Domain specific (dis-)similarity or proximity measures used e.g. in alignment algorithms of sequence data are popular to analyze complicated data objects and to cover domain specific data properties. Without an underlying vector space these data are given as pairwise (dis-)similarities only. The few available methods for such data focus widely on similarities and do not scale to large datasets. Kernel methods are very effective for metric similarity matrices, also at large scale, but costly transformations are necessary starting with non-metric (dis-) similarities. We propose an integrative combination of Nystr{\"{o}}m approximation, potential double centering and eigenvalue correction to obtain valid kernel matrices at linear costs in the number of samples. By the proposed approach effective kernel approaches become accessible. Experiments with several larger (dis-)similarity datasets show that the proposed method achieves much better runtime performance than the standard strategy while keeping competitive model accuracy. The main contribution is an efficient and accurate technique, to convert (potentially non-metric) large scale dissimilarity matrices into approximated positive semi-definite kernel matrices at linear costs.},
author = {Gisbrecht, Andrej and Schleif, Frank Michael},


issn = {18728286},
journal = {Neurocomputing},
keywords = {Dissimilarity learning,Double centering,Indefinite kernel,Linear eigenvalue correction,Nystr{\"{o}}m approximation,Pseudo-Euclidean},
pages = {643--657},
title = {{Metric and non-metric proximity transformations at linear costs}},
volume = {167},
year = {2015}
}
@article{Schneider2010,
abstract = {We present two approaches to extend Robust Soft Learning Vector Quantization (RSLVQ). This algorithm for nearest prototype classification is derived from an explicit cost function and follows the dynamics of a stochastic gradient ascent. The RSLVQ cost function is defined in terms of a likelihood ratio and involves a hyperparameter which is kept constant during training. We propose to adapt the hyperparameter in the training phase based on the gradient information. Besides, we propose to base the classifier's decision on the value of the likelihood ratio instead of using the distance based classification approach. Experiments on artificial and real life data show that the hyperparameter crucially influences the performance of RSLVQ. However, it is not possible to estimate the best value from the data prior to learning. We show that the proposed variant of RSLVQ is very robust with respect to the initial value of the hyperparameter. The classification approach based on the likelihood ratio turns out to be superior to distance based classification, if local hyperparameters are adapted for each prototype. {\textcopyright} 2010 Elsevier B.V. All rights reserved.},
author = {Schneider, Petra and Biehl, Michael and Hammer, Barbara},


issn = {09252312},
journal = {Neurocomputing},
keywords = {Cost function,Distance based classification,Hyperparameter,Learning vector quantization,Likelihood,Robust Soft LVQ},
number = {7-9},
pages = {1117--1124},
title = {{Hyperparameter learning in probabilistic prototype-based models}},
volume = {73},
year = {2010}
}
@inproceedings{Cham2018,
abstract = {As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.},
address = {Cham},
author = {Tan, Chuanqi and Sun, Fuchun and Kong, Tao and Zhang, Wenchang and Yang, Chao and Liu, Chunfang},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

editor = {Krurkov{\'{a}}, V{\v{e}}ra and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias},

isbn = {9783030014230},
issn = {16113349},
keywords = {Deep transfer learning,Survey,Transfer learning},
pages = {270--279},
publisher = {Springer International Publishing},
title = {{A survey on deep transfer learning}},
volume = {11141 LNCS},
year = {2018}
}
@article{Karbalayghareh2018,
abstract = {{\textcopyright} 1991-2012 IEEE. Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer learning framework, in the homogeneous transfer learning scenario, where the source and target domains are related through the joint prior density of the model parameters. The modeling of joint prior densities enables better understanding of the 'transferability' between domains. We define a joint Wishart distribution for the precision matrices of the Gaussian feature-label distributions in the source and target domains to act like a bridge that transfers the useful information of the source domain to help classification in the target domain by improving the target posteriors. Using several theorems in multivariate statistics, the posteriors and posterior predictive densities are derived in closed forms with hypergeometric functions of matrix argument, leading to our novel closed-form and fast Optimal Bayesian Transfer Learning (OBTL) classifier. Experimental results on both synthetic and real-world benchmark data confirm the superb performance of the OBTL compared to the other state-of-the-art transfer learning and domain adaptation methods.},
archivePrefix = {arXiv},
arxivId = {1801.00857},
author = {Karbalayghareh, Alireza and Qian, Xiaoning and Dougherty, Edward R.},

eprint = {1801.00857},

issn = {1053587X},
journal = {IEEE Transactions on Signal Processing},
keywords = {Transfer learning,domain adaptation,optimal Bayesian classifier,optimal Bayesian transfer learning},
number = {14},
pages = {3724--3739},
publisher = {IEEE},
title = {{Optimal Bayesian Transfer Learning}},
volume = {66},
year = {2018}
}
@inproceedings{10.1007/978-3-030-10928-8_46,
abstract = {Transfer learning algorithms utilize knowledge from a data-rich source domain to learn a model in the target domain where labeled data is scarce. This paper presents a novel solution for the challenging and interesting problem of Heterogeneous Transfer Learning (HTL) where the source and target task have heterogeneous feature and label spaces. Contrary to common space based HTL algorithms, the proposed HTL algorithm adapts source data for the target task. The correspondence required for aligning the heterogeneous features of the source and target domain is obtained through labels across two domains that are semantically aligned using web-induced knowledge. The experimental results suggest that the proposed algorithm performs significantly better than state-of-the-art transfer approaches on three diverse real-world transfer tasks.},
address = {Cham},
author = {Sukhija, Sanatan and Krishnan, Narayanan C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

editor = {Berlingerio, Michele and Bonchi, Francesco and G{\"{a}}rtner, Thomas and Hurley, Neil and Ifrim, Georgiana},

isbn = {9783030109271},
issn = {16113349},
keywords = {Heterogeneous transfer learning,Sample selection},
pages = {777--793},
publisher = {Springer International Publishing},
title = {{Web-induced heterogeneous transfer learning with sample selection}},
volume = {11052 LNAI},
year = {2019}
}
@article{Hofleitner.2012,
abstract = {This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming GPS probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile Millennium system since March 2009 with over 500 probe vehicles reporting their position once per minute in San Francisco, CA.The hybrid model provides a distinct advantage over pure statistical or pure traffic theory models in that it is robust to noisy data (due to the large volumes of historical data) and it produces forecasts using traffic flow theory principles consistent with the physics of traffic. Validation of the model is performed in two different ways. First, a large scale test of the model is performed by splitting the data source into two sets, using the first to produce the estimates and the second to validate them. Second, an alternate validation approach is presented. It consists of a 3-day experiment in which GPS data was collected once per second from 20 drivers on four routes through San Francisco, allowing for precise calculation of actual travel times. The model is run by down-sampling the data and validated using the travel times from these 20 drivers. The results indicate that this approach is a significant step forward in estimating traffic states throughout the arterial network using a relatively small amount of real-time data. The estimates from our model are compared to those given by a data-driven baseline algorithm, for which we achieve a 16{\%} improvement in terms of the root mean squared error of travel time estimates. The primary reason for success is the reliance on a flow model of traffic, which ensures that estimates are consistent with the physics of traffic. {\textcopyright} 2012 Elsevier Ltd.},
author = {Hofleitner, Aude and Herring, Ryan and Bayen, Alexandre},


issn = {01912615},
journal = {Transportation Research Part B: Methodological},
keywords = {Arterial traffic,Estimation,Forecast,GPS probe data,Machine learning,Streaming data},
number = {9},
pages = {1097--1122},
title = {{Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning}},

volume = {46},
year = {2012}
}
@incollection{Semenovich2011,
author = {Ghifary, Muhammad and Kleijn, W. Bastiaan and Zhang, Mengjie},


keywords = {domain adaptation,learning,maximum mean discrepancy,neural networks,representation,transfer learning},
number = {2},
pages = {898--904},
title = {{Domain Adaptive Neural Networks for Object Recognition}},

volume = {35},
year = {2014}
}
@article{Vapnik2017,
abstract = {The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.},
author = {Vapnik, Vladimir and Izmailov, Rauf},


isbn = {9550151050},
issn = {15737470},
journal = {Annals of Mathematics and Artificial Intelligence},
keywords = {Classification,Frames,Intelligent teacher,Knowledge representation,Knowledge transfer,Learning theory,Neural network,Privileged information,Regression,Similarity control,Support vector machine},
number = {1-2},
pages = {3--19},
publisher = {Annals of Mathematics and Artificial Intelligence},
title = {{Knowledge transfer in SVM and neural networks}},
volume = {81},
year = {2017}
}
@inproceedings{DBLP:conf/iclr/ZhangBHRV17,
author = {Zhang, Chiyuan and Bengio, Samy and Hardt, Moritz and Recht, Benjamin and Vinyals, Oriol},
booktitle = {5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings},

title = {{Understanding deep learning requires rethinking generalization}},

year = {2017}
}
@article{8742904,
author = {Cai, Long and Gu, Jie and Jin, Zhijian},


issn = {1551-3203},
journal = {IEEE Transactions on Industrial Informatics},
keywords = {latent parameter,short-term load forecasting,transfer learning,weight assignment},
pages = {1--1},
title = {{Two-layer Transfer-learning-based Architecture for Short-Term Load Forecasting}},
year = {2019}
}
@incollection{NIPS2014_5347,
author = {Yosinski, Jason and Clune, Jeff and Bengio, Yoshua and Lipson, Hod},
booktitle = {Advances in Neural Information Processing Systems 27},
editor = {Ghahramani, Z and Welling, M and Cortes, C and Lawrence, N D and Weinberger, K Q},

pages = {3320--3328},
publisher = {Curran Associates, Inc.},
title = {{How transferable are features in deep neural networks?}},

year = {2014}
}
@incollection{Biehl2019,
abstract = {We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments. Specifically, we study Learning Vector Quantization (LVQ) systems trained from a stream of high-dimensional, clustered data.We consider standard winner-takes-all updates known as LVQ1. Statistical properties of the input data change on the time scale defined by the training process. We apply analytical methods borrowed from statistical physics which have been used earlier for the exact description of learning in stationary environments. The suggested framework facilitates the computation of learning curves in the presence of virtual and real concept drift. Here we focus on timedependent class bias in the training data. First results demonstrate that, while basic LVQ algorithms are suitable for the training in non-stationary environments, weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes.},
archivePrefix = {arXiv},
arxivId = {1903.07273},
author = {Biehl, Michael and Abadi, Fthi and G{\"{o}}pfert, Christina and Hammer, Barbara},
booktitle = {Advances in Intelligent Systems and Computing},

eprint = {1903.07273},

isbn = {9783030196417},
issn = {21945357},
keywords = {Concept drift,LVQ,Supervised learning,Weight decay},
pages = {210--221},
title = {{Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework}},

volume = {976},
year = {2020}
}
@article{Gomes2019,
author = {Gomes, Heitor Murilo and Read, Jesse and Bifet, Albert and Barddal, Jean Paul and Gama, Jo{\~{a}}o},


issn = {19310145},
journal = {ACM SIGKDD Explorations Newsletter},
month = {nov},
number = {2},
pages = {6--22},
title = {{Machine learning for streaming data}},

volume = {21},
year = {2019}
}
@article{Seo2003,
abstract = {We propose a new method for the construction of nearest prototype classifiers which is based on a Gaussian mixture ansatz and which can be interpreted as an annealed version of learning vector quantization (LVQ). The algorithm performs a gradient descent on a cost-function minimizing the classification error on the training set. We investigate the properties of the algorithm and assess its performance for several toy data sets and for an optical letter classification task. Results show 1) that annealing in the dispersion parameter of the Gaussian kernels improves classification accuracy; 2) that classification results are better than those obtained with standard learning vector quantization (LVQ 2.1, LVQ 3) for equal numbers of prototypes; and 3) that annealing of the width parameter improved the classification capability. Additionally, the principled approach provides an explanation of a number of features of the (heuristic) LVQ methods.},
author = {Seo, Sambu and Bode, Mathias and Obermayer, Klaus},


isbn = {1045-9227},
issn = {10459227},
journal = {IEEE Transactions on Neural Networks},
keywords = {Gaussian mixture model,Learning vector quantization (LVQ),Multiclass classification,Soft nearest prototype classification},
number = {2},
pages = {390--398},
pmid = {18238021},
title = {{Soft nearest prototype classification}},
volume = {14},
year = {2003}
}
@inproceedings{Ma_2019_CVPR,
author = {Ma, Xinhong and Zhang, Tianzhu and Xu, Changsheng},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation}},
year = {2019}
}
@article{Su2009,
author = {Su, Bai and Shen, Yi-Dong},


isbn = {9781605583266},
journal = {Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC '09},
pages = {957},
title = {{Maximum margin transfer learning}},

year = {2009}
}
@article{Song2002,
abstract = {We generalize the notion of PAC learning to include transfer learning. In our framework, the linkage between the source and the target tasks is a result of hav-ing the sample distribution of all classes drawn from the same distribution of dis-tributions, and by restricting all source and a target concepts to belong to the same hypothesis subclass. We have two models: an adversary model and a randomized model. In the adversary model, we show that for binary classification, conventional PAC-learning is equivalent to the new notion of PAC-transfer and to transfer generaliza-tion of the VC-dimension. For regression, we show that PAC-transferability may exist even in the absence of PAC-learning. In the randomized model, we provide PAC-Bayesian and VC-style generalization bounds to transfer learning, including bounds specifically derived for Deep Learning. A wide discussion on the tradeoffs between the different involved parameters in the bounds is provided. We demonstrate both cases in which transfer does not reduce the sample size (" trivial transfer ") and cases in which the sample size is reduced (" non-trivial transfer ").},
author = {Galanti, Tomer and Wolf, Lior and Hazan, Tamir},


issn = {2049-8764},
journal = {Information and Inference},
month = {jun},
number = {2},
pages = {159--209},
title = {{A theoretical framework for deep transfer learning}},

volume = {5},
year = {2016}
}
@article{Schleif2017,
abstract = {The recently proposed Krĕin space Support Vector Machine (KSVM) is an efficient classifier for indefinite learning problems, but with quadratic to cubic complexity and a non-sparse decision function. In this paper a Krĕin space Core Vector Machine (iCVM) solver is derived. A sparse model with linear runtime complexity can be obtained under a low rank assumption. The obtained iCVM models can be applied to indefinite kernels without additional preprocessing. Using iCVM one can solve CVM with usually troublesome kernels having large negative eigenvalues or large numbers of negative eigenvalues. Experiments show that our algorithm is similar efficient as the Krĕin space Support Vector Machine but with substantially lower costs, such that also large scale problems can be processed.},
author = {Schleif, Frank Michael and Tino, Peter},


issn = {00313203},
journal = {Pattern Recognition},
keywords = {Classification,Core Vector Machine,Indefinite learning,Krĕin space,Linear complexity,Nystr{\"{o}}m,Sparse},
pages = {187--195},
publisher = {Elsevier Ltd},
title = {{Indefinite Core Vector Machine}},

volume = {71},
year = {2017}
}
@article{Wang2018b,
abstract = {Transfer learning has been proven effective when within-target labeled data is scarce. A lot of works have developed successful algorithms and empirically observed positive transfer effect that improves target generalization error using source knowledge. However, theoretical analysis of transfer learning is more challenging due to the nature of the problem and thus is less studied. In this report, we do a survey of theoretical works in transfer learning and summarize key theoretical guarantees that prove the effectiveness of transfer learning. The theoretical bounds are derived using model complexity and learning algorithm stability. As we should see, these works exhibit a trade-off between tight bounds and restrictive assumptions. Moreover, we also prove a new generalization bound for the multi-source transfer learning problem using the VC-theory, which is more informative than the one proved in previous work.},
archivePrefix = {arXiv},
arxivId = {1810.05986},
author = {Wang, Zirui},
eprint = {1810.05986},

journal = {CoRR},
month = {oct},
number = {usually iid},
title = {{Theoretical Guarantees of Transfer Learning}},

year = {2018}
}
@article{Afridi2018a,
abstract = {Transfer learning, or inductive transfer, refers to the transfer of knowledge from a source task to a target task. In the context of convolutional neural networks (CNNs), transfer learning can be implemented by transplanting the learned feature layers from one CNN (derived from the source task) to initialize another (for the target task). Previous research has shown that the choice of the source CNN impacts the performance of the target task. In the current literature, there is no principled way for selecting a source CNN for a given target task despite the increasing availability of pre-trained source CNNs. In this paper we investigate the possibility of automatically ranking source CNNs prior to utilizing them for a target task. In particular, we present an information theoretic framework to understand the source-target relationship and use this as a basis to derive an approach to automatically rank source CNNs in an efficient, zero-shot manner. The practical utility of the approach is thoroughly evaluated using the Places-MIT dataset, MNIST dataset and a real-world MRI database. Experimental results demonstrate the efficacy of the proposed ranking method for transfer learning.},
author = {Afridi, Muhammad Jamal and Ross, Arun and Shapiro, Erik M.},


issn = {00313203},
journal = {Pattern Recognition},
keywords = {CNN selection,Deep learning,Transfer learning},
month = {jan},
pages = {65--75},
publisher = {Elsevier Ltd},
title = {{On automated source selection for transfer learning in convolutional neural networks}},

volume = {73},
year = {2018}
}
@inproceedings{WangEasyTL,
annote = {Easy Transfer: Paremter Free TL Method with two main building blocks:
Intra Domain Programming:
Learns a probability matrix which represents class probabilities.
These probabilities learned that they minimize the probability that a point comes from a far away a source class cluster.
All contrains are made to that it is consistent probability.

Intra Domain Alignment:
Other term for unsupervised domain adaptation which only aligns marginal probability distribution.
Transfer feature learning methods as optional building blog before intra domain programming.},
author = {Wang, Jindong and Chen, Yiqiang and Yu, Han and Huang, Meiyu and Yang, Qiang},
booktitle = {2019 IEEE International Conference on Multimedia and Expo (ICME)},

isbn = {978-1-5386-9552-4},
month = {jul},
pages = {1210--1215},
publisher = {IEEE},
title = {{Easy Transfer Learning By Exploiting Intra-Domain Structures}},

year = {2019}
}
@article{Sanodiya2019,
author = {Sanodiya, Rakesh Kumar and Mathew, Jimson and Paul, Biju and Jose, Bijoy A},


issn = {2169-3536},
journal = {IEEE Access},
pages = {181381--181395},
publisher = {IEEE},
title = {{A Kernelized Unified Framework for Domain Adaptation}},

volume = {7},
year = {2019}
}
@inproceedings{Jang2018,
abstract = {Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. Along with its static version, incremental SVD has been used to deal with multiple semi-infinite time series data and to identify patterns of the data. However, existing SVD methods for the multiple time series data analysis do not provide functionality for detecting patterns of data in an arbitrary time range: standard SVD requires data for all intervals corresponding to a time range query, and incremental SVD does not consider an arbitrary time range. In this paper, we propose Zoom-SVD, a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range. Zoom-SVD incrementally compresses multiple time series data block by block to reduce the space cost in storage phase, and efficiently computes singular value decomposition (SVD) for a given time range query in query phase by carefully stitching stored SVD results. Through extensive experiments, we demonstrate that Zoom-SVD is up to 15 faster, and requires 15 less space than existing methods. Our case study shows that Zoom-SVD is useful for capturing past time ranges whose patterns are similar to a query time range.},
address = {New York, NY, USA},
archivePrefix = {arXiv},
arxivId = {1805.00754},
author = {Jang, Jun-Gi and Choi, Dongjin and Jung, Jinhong and Kang, U.},
booktitle = {Proceedings of the 27th ACM International Conference on Information and Knowledge Management},

eprint = {1805.00754},

isbn = {9781450360142},
keywords = {Multiple time series data,Singular value decomposition,Time range query},
month = {oct},
pages = {1083--1092},
publisher = {ACM},
title = {{Zoom-SVD}},

year = {2018}
}
@article{GittensM16,
abstract = {We reconsider randomized algorithms for the low-rank approximation of symmetric positive semi-definite (SPSD) matrices such as Laplacian and kernel matrices that arise in data analysis and machine learning applications. Our main results consist of an empirical evaluation of the performance quality and running time of sampling and projection methods on a diverse suite of SPSD matrices. Our results highlight complementary aspects of sampling versus projection methods; they characterize the effects of common data preprocessing steps on the performance of these algorithms; and they point to important differences between uniform sampling and nonuniform sampling methods based on leverage scores. In addition, our empirical results illustrate that existing theory is so weak that it does not provide even a qualitative guide to practice. Thus, we complement our empirical results with a suite of worst-case theoretical bounds for both random sampling and random projection methods. These bounds are qualitatively superior to existing bounds---e.g. improved additive-error bounds for spectral and Frobenius norm error and relative-error bounds for trace norm error---and they point to future directions to make these algorithms useful in even larger-scale machine learning applications.},
archivePrefix = {arXiv},
arxivId = {1303.1849},
author = {Gittens, Alex and Mahoney, Michael W.},
eprint = {1303.1849},
journal = {Journal of Machine Learning Research},
month = {mar},
pages = {117:1----117:65},
title = {{Revisiting the Nystrom Method for Improved Large-Scale Machine Learning}},

volume = {17},
year = {2013}
}
@article{Losing2017a,
abstract = {Data Mining in non-stationary data streams is gaining more attention recently, especially in the context of Internet of Things and Big Data. It is a highly challenging task, since the fundamentally different types of possibly occurring drift undermine classical assumptions such as data independence or stationary distributions. Available algorithms are either struggling with certain forms of drift or require a priori knowledge in terms of a task specific setting. $\backslash$$\backslash$ We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm since kNN constitutes a proven classifier within the streaming setting. SAM-kNN can deal with heterogeneous concept drift, i.e different drift types and rates, using biologically inspired memory models and their coordination. It can be easily applied in practice since an optimization of the meta parameters is not necessary. The basic idea is to construct dedicated models for the current and former concepts and apply them according to the demands of the given situation. $\backslash$$\backslash$ An extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics as well as real world datasets is conducted. Thereby, we explicitly add new benchmarks enabling a precise performance evaluation on multiple types of drift. The highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift.},
author = {Losing, Viktor and Hammer, Barbara and Wersing, Heiko},


isbn = {9781509054725},
issn = {15504786},
journal = {Proceedings - IEEE International Conference on Data Mining, ICDM},
pages = {291--300},
title = {{KNN classifier with self adjusting memory for heterogeneous concept drift}},
volume = {1},
year = {2017}
}
@book{Kohonen1997,
address = {Berlin, Heidelberg},
editor = {Kohonen, Teuvo},
isbn = {3-540-62017-6},
publisher = {Springer-Verlag},
title = {{Self-organizing Maps}},
year = {1997}
}
@article{Gama2014,
abstract = {Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, overview the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts and practitioners.},
archivePrefix = {arXiv},
arxivId = {1010.4784},
author = {Gama, Jo{\~{a}}o and {\v{Z}}liobaitė, Indrė and Bifet, Albert and Pechenizkiy, Mykola and Bouchachia, Abdelhamid},

eprint = {1010.4784},

isbn = {1011158908},
issn = {03600300},
journal = {ACM Computing Surveys},
number = {4},
pages = {1--37},
pmid = {21824845},
title = {{A survey on concept drift adaptation}},

volume = {46},
year = {2014}
}
@article{Kim2014,
abstract = {We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.},
archivePrefix = {arXiv},
arxivId = {arXiv:1408.5882v2},
author = {Kim, Yoon},
eprint = {arXiv:1408.5882v2},

isbn = {9781937284961},
journal = {EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference},
pages = {1746--1751},
title = {{Convolutional neural networks for sentence classification}},
year = {2014}
}
@inproceedings{4587637,
abstract = {To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems we observe that a category might be learnable using only a small subset of reference prototypes. Related problems may share a significant number of relevant prototypes; we find such a concise representation by performing a joint loss minimization over the training sets of related problems with a shared regularization penalty that minimizes the total number of prototypes involved in the approximation. This optimization problem can be formulated as a linear program that can be solved efficiently. We conduct experiments on a news-topic prediction task where the goal is to predict whether an image belongs to a particular news topic. Our results show that when only few examples are available for training a target topic, leveraging knowledge learnt from other topics can significantly improve performance. {\textcopyright}2008 IEEE.},
author = {Quattoni, Ariadna and Collins, Michael and Darrell, Trevor},
booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},


isbn = {9781424422432},
issn = {1063-6919},
keywords = {Approximation methods,Boosting,Computer vision,Image classification,Kernel,Learning systems,Optimization methods,Prototypes,Semisupervised learning,Training data,image classification,image representation,joint loss minimization,kernel function,learning (artificial intelligence),linear program,linear programming,minimisation,transfer learning},
pages = {1--8},
title = {{Transfer learning for image classification with sparse prototype representations}},
year = {2008}
}
@article{Geer2004,
author = {Geer, Ruth},

pages = {1--21},
title = {{Effective Blended learning Practices : Evidence-based Perspectives in ICT- Facilitated Education Section 1 : Strategies for Teaching and learning}},
year = {2004}
}
@article{Cai2020,
author = {Cai, Ruichu and Li, Jiahao and Zhang, Zhenjie and Yang, Xiaoyan and Hao, Zhifeng},


issn = {2162-237X},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {1--13},
title = {{DACH: Domain Adaptation Without Domain Information}},

year = {2020}
}
@article{Bergstra2012a,
abstract = {Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a comparison with a large previous study that used grid search and manual search to configure neural networks and deep belief networks. Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Granting random search the same computational budget, random search finds better models by effectively searching a larger, less promising configuration space. Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search over the same 32-dimensional configuration space found statistically equal performance on four of seven data sets, and superior performance on one of seven. A Gaussian process analysis of the function from hyper-parameters to validation set performance reveals that for most data sets only a few of the hyper-parameters really matter, but that different hyper-parameters are important on different data sets. This phenomenon makes grid search a poor choice for configuring algorithms for new data sets. Our analysis casts some light on why recent "High Throughput" methods achieve surprising success-they appear to search through a large number of hyper-parameters because most hyper-parameters do not matter much. We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural base-line against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms.},
author = {Bergstra, James and Bengio, Yoshua},

issn = {15324435},
journal = {Journal of Machine Learning Research},
keywords = {Deep learning,Global optimization,Model selection,Neural networks,Response surface modeling},
pages = {281--305},
title = {{Random search for hyper-parameter optimization}},
volume = {13},
year = {2012}
}
@inproceedings{kifer2004detecting,
author = {Kifer, Daniel and Ben-David, Shai and Gehrke, Johannes},
booktitle = {Proceedings of the Thirtieth International Conference on Very Large Data Bases - Volume 30},
doi = {10.5555/1316689.1316707},
file = {:C$\backslash$:/Users/chris/Documents/Literatur//Kifer, Ben-David, Gehrke{\_}2004{\_}Proceedings of the Thirtieth International Conference on Very Large Data Bases - Volume 30.pdf:pdf},
isbn = {0120884690},
mendeley-groups = {Streaming},
pages = {180--191},
publisher = {VLDB Endowment},
series = {VLDB '04},
title = {{Detecting change in data streams}},
year = {2004}
}
@inproceedings{Li_2020_CVPR,
author = {Li, Mengxue and Zhai, Yi-Ming and Luo, You-Wei and Ge, Peng-Fei and Ren, Chuan-Xian},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mendeley-groups = {Transfer Learning},
pages = {13936--13944},
title = {{Enhanced Transport Distance for Unsupervised Domain Adaptation}},
year = {2020}
}
@inproceedings{Xu2019b,
abstract = {Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions. Previous works may not effectively uncover the underlying reasons that would lead to the drastic model degradation on the target task. In this paper, we empirically reveal that the erratic discrimination of the target domain mainly stems from its much smaller feature norms with respect to that of the source domain. To this end, we propose a novel parameter-free Adaptive Feature Norm approach. We demonstrate that progressively adapting the feature norms of the two domains to a large range of values can result in significant transfer gains, implying that those task-specific features with larger norms are more transferable. Our method successfully unifies the computation of both standard and partial domain adaptation with more robustness against the negative transfer issue. Without bells and whistles but a few lines of code, our method substantially lifts the performance on the target task and exceeds state-of-the-arts by a large margin (11.5{\%} on Office-Home and 17.1{\%} on VisDA2017). We hope our simple yet effective approach will shed some light on the future research of transfer learning. Code is available at https://github.com/jihanyang/AFN.},
author = {Xu, Ruijia and Li, Guanbin and Yang, Jihan and Lin, Liang},
booktitle = {2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
isbn = {978-1-7281-4803-8},
issn = {15505499},
mendeley-groups = {Transfer Learning},
month = {oct},
pages = {1426--1435},
publisher = {IEEE},
title = {{Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation}},
volume = {2019-Octob},
year = {2019}
}

@incollection{NIPS2012_4727,
author = {Gretton, Arthur and Sejdinovic, Dino and Strathmann, Heiko and Balakrishnan, Sivaraman and Pontil, Massimiliano and Fukumizu, Kenji and Sriperumbudur, Bharath K},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {Pereira, F and Burges, C J C and Bottou, L and Weinberger, K Q},

pages = {1205--1213},
publisher = {Curran Associates, Inc.},
title = {{Optimal kernel choice for large-scale two-sample tests}},

year = {2012}
}
@inproceedings{8100030,
annote = {Seperate subspace projector for source and target
={\textgreater} MMD with two projectors.
Between and Intra-Class Variance is used
Subspace operators small to each other.
Kernelized is not usedful

Pseudolabeling:
Create class-depend MMD Matrix.
Predict labels
Update class-depend MMD matrix


Hence, the variance maximization can be achieved as follows:
maxTr(BTStB)
St = XtHtXT
Ht is scatter matrix for optimization problem},
author = {Zhang, Jing and Li, Wanqing and Ogunbona, Philip},
booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},


isbn = {978-1-5386-0457-1},
keywords = {Image recognition,JGSA,Joint Geometrical and Statistical Alignment,Linear programming,Minimization,Pattern recognition,Training,Visualization,coupled projections,cross-domain visual recognition tasks,distribution shift,geometrical shift,image recognition,image representation,low-dimensional subspaces,source domain,statistical analysis,target domain data,unsupervised domain adaptation method,unsupervised learning,visual domain adaptation},
month = {jul},
pages = {5150--5158},
publisher = {IEEE},
title = {{Joint Geometrical and Statistical Alignment for Visual Domain Adaptation}},

year = {2017}
}
@incollection{NIPS2019_8688,
author = {Lee, Joshua and Sattigeri, Prasanna and Wornell, Gregory},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {4372--4382},
publisher = {Curran Associates, Inc.},
title = {{Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks}},

year = {2019}
}
@incollection{NIPS2004_2740,
author = {Grandvalet, Yves and Bengio, Yoshua},
booktitle = {Advances in Neural Information Processing Systems 17},
editor = {Saul, L K and Weiss, Y and Bottou, L},

pages = {529--536},
publisher = {MIT Press},
title = {{Semi-supervised Learning by Entropy Minimization}},

year = {2005}
}
@article{Tropp2017,
abstract = {Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates. Because of storage limitations, it may only be possible to retain a sketch of the psd matrix. This paper develops a new algorithm for fixed-rank psd approximation from a sketch. The approach combines the Nystrom approximation with a novel mechanism for rank truncation. Theoretical analysis establishes that the proposed method can achieve any prescribed relative error in the Schatten 1-norm and that it exploits the spectral decay of the input matrix. Computer experiments show that the proposed method dominates alternative techniques for fixed-rank psd matrix approximation across a wide range of examples.},
archivePrefix = {arXiv},
arxivId = {1706.05736},
author = {Tropp, Joel A. and Yurtsever, Alp and Udell, Madeleine and Cevher, Volkan},
eprint = {1706.05736},

issn = {10495258},
journal = {dvances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December},
number = {Nips},
pages = {1--10},
title = {{Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data}},

year = {2017}
}
@article{Nova2016,
author = {Mer{\'{e}}nyi, Erzs{\'{e}}bet and Mendenhall, Michael J. and O'Driscoll, Patrick},


isbn = {9783319285177},
issn = {21945357},
journal = {Advances in Intelligent Systems and Computing},
keywords = {generalization ability,learning vector quantization},
pages = {305--314},
title = {{Advances in self-organizing maps and learning vector quantization: Proceedings of the 11th International workshop WSOM 2016, Houston, Texas, USA, january 6−8, 2016}},

volume = {428},
year = {2016}
}
@article{Shao2014,
abstract = {Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams},
annote = {Concept Drift:
Between two chunks of data, the angels of the class wise covariance eigenvectors are compared
if the maximum angle between any of the vectors is higher than 60 concepct drift is detected.},
author = {Shao, Junming and Ahmadi, Zahra and Kramer, Stefan},


isbn = {9781450329569},
journal = {Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14},
keywords = {classification,concept drift,data stream,synchronization},
pages = {412--421},
title = {{Prototype-based learning on concept-drifting data streams}},

year = {2014}
}
@article{Nemtsov2016,
abstract = {The Nystr$\backslash$"{\{}o{\}}m method is routinely used for out-of-sample extension of kernel matrices. We describe how this method can be applied to find the singular value decomposition (SVD) of general matrices and the eigenvalue decomposition (EVD) of square matrices. We take as an input a matrix {\$}M\backslashin \backslashmathbb{\{}R{\}}{\^{}}{\{}m\backslashtimes n{\}}{\$}, a user defined integer {\$}s\backslashleq min(m,n){\$} and {\$}A{\_}M \backslashin \backslashmathbb{\{}R{\}}{\^{}}{\{}s\backslashtimes s{\}}{\$}, a matrix sampled from the columns and rows of {\$}M{\$}. These are used to construct an approximate rank-{\$}s{\$} SVD of {\$}M{\$} in {\$}O\backslashleft(s{\^{}}2\backslashleft(m+n\backslashright)\backslashright){\$} operations. If {\$}M{\$} is square, the rank-{\$}s{\$} EVD can be similarly constructed in {\$}O\backslashleft(s{\^{}}2 n\backslashright){\$} operations. Thus, the matrix {\$}A{\_}M{\$} is a compressed version of {\$}M{\$}. We discuss the choice of {\$}A{\_}M{\$} and propose an algorithm that selects a good initial sample for a pivoted version of {\$}M{\$}. The proposed algorithm performs well for general matrices and kernel matrices whose spectra exhibit fast decay.},
archivePrefix = {arXiv},
arxivId = {1305.0203},
author = {Nemtsov, Arik and Averbuch, Amir and Schclar, Alon},

eprint = {1305.0203},

isbn = {0889-1591},
issn = {15714128},
journal = {Intelligent Data Analysis},
keywords = {Compression,EVD,Nystrom,SVD,out-of-sample extension},
month = {may},
number = {5},
pages = {997--1019},
pmid = {21333735},
title = {{Matrix compression using the Nystr{\"{o}}m method}},

volume = {20},
year = {2016}
}
@inproceedings{Street.2001,
abstract = {Ensemble methods have recently garnered a great deal of attention in the machine learning community. Techniques such as Boosting and Bagging have proven to be highly effective but require repeated resampling of the training data, making them inappropriate in a data mining context. The methods presented in this paper take advantage of plentiful data, building separate classifiers on sequential chunks of training points. These classifiers are combined into a fixed-size ensemble using a heuristic replacement strategy. The result is a fast algorithm for large-scale or streaming data that classifies as well as a single decision tree built on all the data, requires approximately constant memory, and adjusts quickly to concept drift.},
author = {Street, W. Nick and Kim, YongSeog},
booktitle = {Proceedings of the seventh {\{}ACM{\}} {\{}SIGKDD{\}} international conference on Knowledge discovery and data mining, San Francisco, CA, USA, August 26-29, 2001},


pages = {377--382},
title = {{A streaming ensemble algorithm (SEA) for large-scale classification}},

year = {2004}
}
@inproceedings{Wozniak.2017,
abstract = {Nowadays, most of the data comes continuously and its distribution may change over the time. Unfortunately, most of classifiers assume that statistical characteristics of used predicting model are not being changed. This work presents modification of the previously proposed Weighted Aging Classifier Ensemble (WAE), called Accuracy Based WAE (AB-WAE), which can easily adapt to the probability characteristic changes caused by so-called concept drift. The proposed model does not exploit a drift detection mechanism, but AB-WAE tries to change the line-up of the classifier ensemble and weights assigned to base classifiers. Thus, one individual classifier is trained on the basis of the each incoming data chunk, then AB-WAE chooses the most valuable ensemble taking into consideration: fixed ensemble size, the previously trained models and new trained classifier. The discussed WAE modification uses the ensemble of homogeneous classifiers only, what allows to employ more sophisticated combination rule based on support functions, what should boost the classification accuracy, what was confirmed by the computer experiments.},
author = {Wozniak, Michal},
booktitle = {IEEE 4th International Conference on Soft Computing and Machine Intelligence, ISCMI 2017},


isbn = {9781538613146},
keywords = {classifier ensemble,concept drift,data stream classification,machine learning,pattern classification},
month = {nov},
pages = {21--24},
title = {{Accuracy based weighted aging ensemble (AB-WAE) - Algorithm for data stream classification}},
volume = {2018-Janua},
year = {2018}
}
@inproceedings{Ni2013,
abstract = {Domain adaptation addresses the problem where data instances of a $\backslash$nsource domain have different distributions from that of a target domain, which $\backslash$noccurs frequently in many real life scenarios. This work focuses on unsupervised $\backslash$ndomain adaptation, where labeled data are only available in the source domain. $\backslash$nWe propose to interpolate subspaces through dictionary learning to link the $\backslash$nsource and target domains. These subspaces are able to capture the intrinsic $\backslash$ndomain shift and form a shared feature representation for cross domain $\backslash$nrecognition. Further, we introduce a quantitative measure to characterize the $\backslash$nshift between two domains, which enables us to select the optimal domain to $\backslash$nadapt to the given multiple source domains. We present experiments on face $\backslash$nrecognition across pose, illumination and blur variations, cross dataset object $\backslash$nrecognition, and report improved performance over the state of the art.},
author = {Ni, Jie and Qiu, Qiang and Chellappa, Rama},
booktitle = {2013 IEEE Conference on Computer Vision and Pattern Recognition},


isbn = {978-0-7695-4989-7},
issn = {10636919},
month = {jun},
pages = {692--699},
publisher = {IEEE},
title = {{Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation}},

year = {2013}
}
@misc{Hoeffding1963,
author = {Hoeffding, Wassilfy},

title = {{Hoeffding.pdf}},
year = {1963}
}
@book{Handke2015,
author = {Handke, J{\"{u}}rgen},
booktitle = {Handbuch Hochschullehre Digital},


isbn = {978-3-8288-3419-4},
title = {{Handbuch Hochschullehre Digital}},
year = {2018}
}
@inproceedings{Venkateswara2017,
abstract = {In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.},
annote = {OFFICE-HOME Paper},
archivePrefix = {arXiv},
arxivId = {1706.07522},
author = {Venkateswara, Hemanth and Eusebio, Jose and Chakraborty, Shayok and Panchanathan, Sethuraman},
booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

eprint = {1706.07522},

isbn = {978-1-5386-0457-1},
month = {jul},
pages = {5385--5394},
publisher = {IEEE},
title = {{Deep Hashing Network for Unsupervised Domain Adaptation}},

volume = {2017-Janua},
year = {2017}
}
@inproceedings{10.1007/978-3-030-10928-8_49,
abstract = {Transfer learning in Bayesian optimisation is a popular way to alleviate “cold start” issue. However, most of the existing transfer learning algorithms use overall function space similarity, not a more aligned similarity measure for Bayesian optimisation based on the location of the optima. That makes these algorithms fragile to noisy perturbations, and even simple scaling of function values. In this paper, we propose a robust transfer learning based approach that transfer knowledge of the optima using a consistent probabilistic framework. From the finite samples for both source and target, a distribution on the optima is computed and then divergence between these distributions are used to compute similarities. Based on the similarities a mixture distribution is constructed, which is then used to build a new information-theoretic acquisition function in a manner similar to Predictive Entropy Search (PES). The proposed approach also offers desirable “no bias” transfer learning in the limit. Experiments on both synthetic functions and a set of hyperparameter tuning tests clearly demonstrate the effectiveness of our approach compared to the existing transfer learning methods. Code related to this paper is available at: https://github.com/AnilRamachandran/ITTLBO.git and Data related to this paper is available at: https://doi.org/10.7910/DVN/LRNLZV.},
address = {Cham},
author = {Ramachandran, Anil and Gupta, Sunil and Rana, Santu and Venkatesh, Svetha},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

editor = {Berlingerio, Michele and Bonchi, Francesco and G{\"{a}}rtner, Thomas and Hurley, Neil and Ifrim, Georgiana},

isbn = {9783030109271},
issn = {16113349},
pages = {827--842},
publisher = {Springer International Publishing},
title = {{Information-theoretic transfer learning framework for bayesian optimisation}},
volume = {11052 LNAI},
year = {2019}
}
@inproceedings{Yi2017,
archivePrefix = {arXiv},
arxivId = {arXiv:1704.02510v4},
author = {Yi, Zili and Zhang, Hao and Tan, Ping and Gong, Minglun},
booktitle = {2017 IEEE International Conference on Computer Vision (ICCV)},

eprint = {arXiv:1704.02510v4},

isbn = {978-1-5386-1032-9},
month = {oct},
pages = {2868--2876},
publisher = {IEEE},
title = {{DualGAN: Unsupervised Dual Learning for Image-to-Image Translation}},

year = {2017}
}
@article{Gopalan,
annote = {Note sure if augmenting},
author = {Gopalan, Raghuraman and Li, Ruonan and Chellappa, Rama},

title = {{Domain{\_}Adaptation{\_}Object{\_}Recogntion.Pdf}}
}
@article{Bacciu2019,
abstract = {The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field.},
archivePrefix = {arXiv},
arxivId = {1912.12693},
author = {Bacciu, Davide and Errica, Federico and Micheli, Alessio and Podda, Marco},
eprint = {1912.12693},

journal = {CoRR},
month = {dec},
number = {December 2019},
title = {{A Gentle Introduction to Deep Learning for Graphs}},

year = {2019}
}
@inproceedings{Kranen.2009,
abstract = {Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.},
author = {Kranen, Philipp and Assent, Ira and Baldauf, Corinna and Seidl, Thomas},
booktitle = {Proceedings - IEEE International Conference on Data Mining, ICDM},


isbn = {9780769538952},
issn = {15504786},
keywords = {Anytime algorithms,Self-adaptive algorithms,Stream clustering},
pages = {249--258},
title = {{Self-adaptive anytime stream clustering}},

year = {2009}
}
@article{xue2000molecular,
author = {Xue, Ling and Bajorath, Jurgen},

issn = {13862073},
journal = {Combinatorial Chemistry {\&} High Throughput Screening},
month = {oct},
number = {5},
pages = {363--372},
publisher = {Bentham Science Publishers},
title = {{Molecular Descriptors in Chemoinformatics, Computational Combinatorial Chemistry, and Virtual Screening}},

volume = {3},
year = {2000}
}
@incollection{NIPS2004_2566,
author = {Goldberger, Jacob and Hinton, Geoffrey E and Roweis, Sam T and Salakhutdinov, Russ R},
booktitle = {Advances in Neural Information Processing Systems 17},
editor = {Saul, L K and Weiss, Y and Bottou, L},

pages = {513--520},
publisher = {MIT Press},
title = {{Neighbourhood Components Analysis}},

year = {2005}
}
@article{Zhang2019a,
abstract = {This paper proposes a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN), which uses the domain-collaborative and domain-adversarial learning for training the neural network. We show that these two learning strategies can be uniformly formulated as a domain classifier learning with positive or negative weights on the losses. Moreover, to further enhance the descriminability in the target domain, we propose Self-Paced CAN (SPCAN), which progressively selects pseudo-labeled target samples for re-training the classifiers. Additionally, we extend our domain adaptation approach for more difficult video action recognition task, which is based on the popular two stream approach but additionally considering the cooperation between the RGB stream and the optical flow stream. We propose the Cooperative SPCAN (CoSPCAN) method to select and reweigh the pseudo labeled target samples of one stream (RGB/Flow) based on the information from another stream (Flow/RGB) in a cooperative way. As a result, our CoSPCAN model is able to exchange the information between the two streams. Comprehensive experiments on different benchmark datasets for the object recognition task and the video action recognition task, show our newly proposed approach achieves state-of-the-art performance, which clearly demonstrate the effectiveness of our proposed approaches for unsupervised domain adaptation.},
author = {Zhang, Weichen and Xu, Dong and Ouyang, Wanli and Li, Wen},


issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {adversarial learning,deep learning,domain adaptation,self-paced learning,transfer learning},
number = {XX},
pages = {1--1},
publisher = {IEEE},
title = {{Self-Paced Collaborative and Adversarial Network for Unsupervised Domain Adaptation}},

volume = {8828},
year = {2020}
}
@article{Schleif2015,
abstract = {{\textcopyright} 2015 IEEE. The probabilistic classification vector machine is a very effective and generic probabilistic and sparse classifier. A recently published incremental version improved the runtime complexity to quadratic costs. We derive the Nystr{\"{o}}m approximation for asymmetric matrices to obtain linear runtime and memory complexity for the incremental probabilistic classification vector machine while keeping similar prediction performance.},
author = {Schleif, F. M. and Chen, H. and Tino, P.},


isbn = {9781479919604},
journal = {Proceedings of the International Joint Conference on Neural Networks},
keywords = {Support vector machines,Xenon},
number = {4},
title = {{Incremental probabilistic classification vector machine with linear costs}},
volume = {2015-Septe},
year = {2015}
}
@article{Sutton2007,
abstract = {Current machine learning is dominated by the idea of convergence to a single solution. This is a natural result of separating the training phase from the testing phase. This paradigm excludes dynamic solutions. Even in stationary environments, memory limitations can induce non-stationarity. This suggests that function approximation solutions can be dramatically improved through tracking.},
author = {Sutton, Richard S. and Koop, Anna and Silver, David},


isbn = {9781595937933},
journal = {Proceedings of the 24th international conference on Machine learning - ICML '07},
pages = {871--878},
title = {{On the role of tracking in stationary environments}},

year = {2008}
}
@inproceedings{Koniusz_2017_CVPR,
author = {Koniusz, Piotr and Tas, Yusuf and Porikli, Fatih},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors}},
year = {2017}
}
@article{Shafer2007,
abstract = {Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability {\$}\backslashepsilon{\$}, together with a method that makes a prediction {\$}\backslashhat{\{}y{\}}{\$} of a label {\$}y{\$}, it produces a set of labels, typically containing {\$}\backslashhat{\{}y{\}}{\$}, that also contains {\$}y{\$} with probability {\$}1-\backslashepsilon{\$}. Conformal prediction can be applied to any method for producing {\$}\backslashhat{\{}y{\}}{\$}: a nearest-neighbor method, a support-vector machine, ridge regression, etc. Conformal prediction is designed for an on-line setting in which labels are predicted successively, each one being revealed before the next is predicted. The most novel and valuable feature of conformal prediction is that if the successive examples are sampled independently from the same distribution, then the successive predictions will be right {\$}1-\backslashepsilon{\$} of the time, even though they are based on an accumulating dataset rather than on independent datasets. In addition to the model under which successive examples are sampled independently, other on-line compression models can also use conformal prediction. The widely used Gaussian linear model is one of these. This tutorial presents a self-contained account of the theory of conformal prediction and works through several numerical examples. A more comprehensive treatment of the topic is provided in "Algorithmic Learning in a Random World", by Vladimir Vovk, Alex Gammerman, and Glenn Shafer (Springer, 2005).},
archivePrefix = {arXiv},
arxivId = {0706.3188},
author = {Shafer, Glenn and Vovk, Vladimir},

eprint = {0706.3188},

isbn = {1532-4435},
issn = {1532-4435},
journal = {arXiv},
keywords = {confidence,on-line compression modeling,on-line learning,prediction regions},
pages = {371--421},
pmid = {25391942},
title = {{A tutorial on conformal prediction}},

volume = {9},
year = {2007}
}
@article{10.5555/3322706.3322707,
author = {Cortes, Corinna and Mohri, Mehryar and Medina, Andr{\'{e}}s Mu{\~{n}}oz},

issn = {1532-4435},
journal = {J. Mach. Learn. Res.},
keywords = {domain adaptation,learning theory},
number = {1},
pages = {1--30},
publisher = {JMLR.org},
title = {{Adaptation Based on Generalized Discrepancy}},
volume = {20},
year = {2019}
}
@inproceedings{Baochen2016,
abstract = {Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, requiring unsupervised adaptation. CORAL [18] is a simple unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation. Here, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL). Experiments on standard benchmark datasets show state-of-the-art performance. Our code is available at: https://github.com/VisionLearningGroup/CORAL.},
address = {Cham},
archivePrefix = {arXiv},
arxivId = {arXiv:1607.01719v1},
author = {Sun, Baochen and Saenko, Kate},
booktitle = {Computer Vision -- ECCV 2016 Workshops},
editor = {Hua, Gang and J{\'{e}}gou, Herv{\'{e}}},
eprint = {arXiv:1607.01719v1},

isbn = {978-3-319-49409-8},
pages = {443--450},
publisher = {Springer International Publishing},
title = {{Deep CORAL: Correlation Alignment for Deep Domain Adaptation}},
year = {2016}
}
@article{Wu2020,
abstract = {Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that entropy minimization only may result into collapsed trivial solutions. In this paper, we propose to avoid trivial solutions by further introducing diversity maximization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.},
archivePrefix = {arXiv},
arxivId = {2002.01690},
author = {Wu, Xiaofu and Hang, Suofei and Zhou, Quan and Yang, Zhen and Zhao, Chunming and Latecki, Longin Jan},
eprint = {2002.01690},
file = {:C$\backslash$:/Users/chris/Documents/Literatur/Wu et al.{\_}2020{\_}Arxiv.pdf:pdf},
journal = {Arxiv},
mendeley-groups = {Transfer Learning},
month = {feb},
pages = {1--9},
title = {{Entropy Minimization vs. Diversity Maximization for Domain Adaptation}},
url = {http://arxiv.org/abs/2002.01690},
year = {2020}
}


@inproceedings{DBLP:conf/icml/GaninL15,
annote = {DANN:
Deep Network with 3 Parts:
Feature Extractor: F
Domain Classifier: D
Label Classifier: Y

F extracts features from data (images) and provides it for D and Y.

D triest to distinguish the samples from source to target domain therefore the loss LD tries to minimize the missclassification of the affilication of source and target data.

Y triest to reduce the standard classification loss on the date w.r.t to the labelled samples.

Overall Loss: L(F;Y) - LD)

Formal objective Minimize loss of label and domain classification.

Domain Adaptation Objective (DAO): However for domain adaptaion: Train F that the loss of D is maximized so that D cannont distinguish between source and target samples.
This leads to invariant features for Y to perform well under domain shift.

Gradient Reverasal Layer:
The gradient of D minimize the seperation error between Source and Target domain if this loss is as it is backprop. to F, F would learn to seperate Target and source. Therefore the Gradient is reversed (gradient mulitplied by -1) that F learns to maximize the Loss of D.},
author = {Ganin, Yaroslav and Lempitsky, Victor S},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning, ICLR 2015, Lille, France, 6-11 July 2015},

pages = {1180--1189},
title = {{Unsupervised Domain Adaptation by Backpropagation}},

year = {2015}
}
@article{SchleifGT18,
abstract = {Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval. Lacking an underlying vector space, the data are given as pairwise similarities only. The few algorithms available for such data do not scale to larger datasets. Focusing on probabilistic batch classifiers, the Indefinite Kernel Fisher Discriminant (iKFD) and the Probabilistic Classification Vector Machine (PCVM) are both effective algorithms for this type of data but, with cubic complexity. Here we propose an extension of iKFD and PCVM such that linear runtime and memory complexity is achieved for low rank indefinite kernels. Employing the Nystr{\"{o}}m approximation for indefinite kernels, we also propose a new almost parameter free approach to identify the landmarks, restricted to a supervised learning problem. Evaluations at several larger similarity data from various domains show that the proposed methods provides similar generalization capabilities while being easier to parametrize and substantially faster for large scale data.},
author = {Schleif, Frank Michael and Gisbrecht, Andrej and Tino, Peter},


issn = {18728286},
journal = {Neurocomputing},
keywords = {Classification,Indefinite kernel,Indefinite learning,Kernel fisher discriminant,Low rank approximation,Minimum enclosing ball,Nystr{\"{o}}m approximation},
pages = {213--226},
title = {{Supervised low rank indefinite kernel approximation using minimum enclosing balls}},

volume = {318},
year = {2018}
}
@inproceedings{He2016,
abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57{\%} error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28{\%} relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC {\&} COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},


isbn = {9781467388504},
issn = {10636919},
pages = {770--778},
title = {{Deep residual learning for image recognition}},

volume = {2016-Decem},
year = {2016}
}
@article{Mitliagkas2013,
abstract = {We consider streaming, one-pass principal component analysis (PCA), in the high-dimensional regime, with limited memory. Here, {\$}p{\$}-dimensional samples are presented sequentially, and the goal is to produce the {\$}k{\$}-dimensional subspace that best approximates these points. Standard algorithms require {\$}O(p{\^{}}2){\$} memory; meanwhile no algorithm can do better than {\$}O(kp){\$} memory, since this is what the output itself requires. Memory (or storage) complexity is most meaningful when understood in the context of computational and sample complexity. Sample complexity for high-dimensional PCA is typically studied in the setting of the {\{}$\backslash$em spiked covariance model{\}}, where {\$}p{\$}-dimensional points are generated from a population covariance equal to the identity (white noise) plus a low-dimensional perturbation (the spike) which is the signal to be recovered. It is now well-understood that the spike can be recovered when the number of samples, {\$}n{\$}, scales proportionally with the dimension, {\$}p{\$}. Yet, all algorithms that provably achieve this, have memory complexity {\$}O(p{\^{}}2){\$}. Meanwhile, algorithms with memory-complexity {\$}O(kp){\$} do not have provable bounds on sample complexity comparable to {\$}p{\$}. We present an algorithm that achieves both: it uses {\$}O(kp){\$} memory (meaning storage of any kind) and is able to compute the {\$}k{\$}-dimensional spike with {\$}O(p \backslashlog p){\$} sample-complexity -- the first algorithm of its kind. While our theoretical analysis focuses on the spiked covariance model, our simulations show that our algorithm is successful on much more general models for the data.},
archivePrefix = {arXiv},
arxivId = {1307.0032},
author = {Mitliagkas, Ioannis and Caramanis, Constantine and Jain, Prateek},
eprint = {1307.0032},

issn = {10495258},
journal = {Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems},
pages = {1--9},
title = {{Memory Limited, Streaming PCA}},

year = {2013}
}
@inproceedings{DBLP:conf/iclr/HsuLK18,
author = {Hsu, Yen-Chang and Lv, Zhaoyang and Kira, Zsolt},
booktitle = {6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings},

title = {{Learning to cluster in order to transfer across domains and tasks}},

year = {2018}
}
@article{Arjovsky2017,
abstract = {How can experts from different activity domains structure and share what they know efficiently? How can experts integrate their knowledge more productively? To answer these questions we propose in this paper a feasible solution: a knowledge management framework as support for a knowledge based system (KBS) development in the small and medium sized enterprises (SMEs). Therefore, this work analyse the state of art of the SMEs knowledge. In addition, in this paper we describe the professional risk assessment as part of risk management process and as essential part of SMEs business and we present how can be a KBS built and used to assess the professional risk at the SMEs level. [ABSTRACT FROM AUTHOR]},
archivePrefix = {arXiv},
arxivId = {1701.07875},
author = {{Plantak Vukovac}, D. and Kirinic, V. and Klicek, B.},

eprint = {1701.07875},

isbn = {9781845937591},
issn = {1701.07875},
journal = {DAAAM International Scientific Book 2010},
pmid = {19963286},
title = {{A Comparison of Usability Evaluation Methods for e-Learning Systems}},

year = {2011}
}
@article{Zhou2014,
abstract = {1 Relying on the pairs of source and target data: parallel data, to learn the transformation matrix G 2 It is not so much work, which is mainly based on msDA. After each layer of mSDA, the additional transformation is learned by least-square root.},
author = {Zhou, Joey Tianyi and Pan, Sinno Jialin and Tsang, Ivor W and Yan, Yan},


isbn = {1577356799},
issn = {1041-4347},
journal = {AAAI Conference on Artificial Intelligence},
keywords = {Novel Machine Learning Algorithms},
pages = {2213--2219},
title = {{Hybrid Heterogeneous Transfer Learning through Deep Learning}},
year = {2014}
}
@inproceedings{Kranen.2012,
abstract = {MOA, unterscheiden Classification und Clustering Methods??},
author = {Kranen, Philipp and Kremer, Hardy and Jansen, Timm and Seidl, Thomas and Bifet, Albert and Holmes, Geoff and Pfahringer, Bernhard and Read, Jesse},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},


isbn = {9783642290343},
issn = {03029743},
number = {PART 2},
pages = {309--313},
title = {{Stream data mining using the MOA framework}},

volume = {7239 LNCS},
year = {2012}
}
@article{Ding2015,
abstract = {Recent researches on transfer learning exploit deep structures for discriminative feature representation to tackle cross-domain disparity. However, few of them are able to joint feature learning and knowledge transfer in a unified deep framework. In this paper, we develop a novel approach, called Deep Low-Rank Coding (DLRC), for transfer learning. Specifically, discriminative low-rank coding is achieved in the guidance of an iterative supervised structure term for each single layer. In this way, both marginal and conditional distributions between two domains intend to be mitigated. In addition, a marginalized denoising feature transformation is employed to guarantee the learned single-layer low-rank coding to be robust despite of corruptions or noises. Finally, by stacking multiple layers of low-rank codings, we manage to learn robust cross-domain features from coarse to fine. Experimental results on several benchmarks have demonstrated the effectiveness of our proposed algorithm on facilitating the recognition performance for the target domain.},
author = {Ding, Zhengming and Shao, Ming and Fu, Yun},

isbn = {9781577357384},
issn = {10450823},
journal = {IJCAI International Joint Conference on Artificial Intelligence},
keywords = {Special Track on Machine Learning},
number = {Ijcai},
pages = {3453--3459},
title = {{Deep low-rank coding for transfer learning}},
volume = {2015-Janua},
year = {2015}
}
@inproceedings{Balaji2019,
author = {Balaji, Yogesh and Chellappa, Rama and Feizi, Soheil},
booktitle = {2019 IEEE/CVF International Conference on Computer Vision (ICCV)},


isbn = {978-1-7281-4803-8},
month = {oct},
pages = {6499--6507},
publisher = {IEEE},
title = {{Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation}},

year = {2019}
}
@article{Chen2014b,
abstract = {Probabilistic classification vector machine (PCVM) is a sparse learning approach aiming to address the stability problems of relevance vector machine for classification problems. Because PCVM is based on the expectation maximization algorithm, it suffers from sensitivity to initialization, convergence to local minima, and the limitation of Bayesian estimation making only point estimates. Another disadvantage is that PCVM was not efficient for large data sets. To address these problems, this paper proposes an efficient PCVM (EPCVM) by sequentially adding or deleting basis functions according to the marginal likelihood maximization for efficient training. Because of the truncated prior used in EPCVM, two approximation techniques, i.e., Laplace approximation and expectation propagation (EP), have been used to implement EPCVM to obtain full Bayesian solutions. We have verified Laplace approximation and EP with a hybrid Monte Carlo approach. The generalization performance and computational effectiveness of EPCVM are extensively evaluated. Theoretical discussions using Rademacher complexity reveal the relationship between the sparsity and the generalization bound of EPCVM. {\textcopyright} 2013 IEEE.},
author = {Chen, Huanhuan and Tino, Peter and Yao, Xin},


issn = {2162237X},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
keywords = {Bayesian classification,Laplace approximation,efficient probabilistic classification model,expectation propagation (EP),incremental learning,support vector machine (SVM)},
number = {2},
pages = {356--369},
publisher = {IEEE},
title = {{Efficient probabilistic classification vector machine with incremental basis function selection}},
volume = {25},
year = {2014}
}
@article{Amini2014,
abstract = {Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the number of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density-based methods in the clustering process and at the same time overcoming the constraints, which are put out by data stream's nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms' performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.},
author = {Amini, Amineh and Wah, Ying and Saboohi, Hadi},


isbn = {1000-9000},
issn = {10009000},
journal = {Journal of Computer Science and Technology},
keywords = {data stream,density-based clustering,grid-based clustering,micro-clustering},
number = {1},
pages = {116--141},
title = {{Amini A, Wah TY, Saboohi H. On density-based data streams clustering algorithms: A survey On Density-Based Data Streams Clustering Algorithms: A Survey}},

volume = {29},
year = {2014}
}
@inproceedings{Shabandar.2017,
abstract = {{\textcopyright} 2017 IEEE. With the rapid advancements in technology, Massive Open Online Courses (MOOCs) have become the most popular form of online educational delivery, largely due to the removal of geographical and financial barriers for participants. A large number of learners globally enrol in such courses. Despite the flexible accessibility, results indicate that the completion rate is quite low. Educational Data Mining and Learning Analytics are emerging fields of research that aim to enhance the delivery of education through the application of various statistical and machine learning approaches. An extensive literature survey indicates that no significant research is available within the area of MOOC data analysis, in particular considering the behavioural patterns of users. In this paper, therefore, two sets of features, based on learner behavioural patterns, were compared in terms of their suitability for predicting the course outcome of learners participating in MOOCs. Our Exploratory Data Analysis demonstrates that there is strong correlation between click stream actions and successful learner outcomes. Various Machine Learning algorithms have been applied to enhance the accuracy of classifier models. Simulation results from our investigation have shown that Random Forest achieved viable performance for our prediction problem, obtaining the highest performance of the models tested. Conversely, Linear Discriminant Analysis achieved the lowest relative performance, though represented only a marginal reduction in performance relative to the Random Forest.},
author = {Al-Shabandar, Raghad and Hussain, Abir and Laws, Andy and Keight, Robert and Lunn, Janet and Radi, Naeem},
booktitle = {Proceedings of the International Joint Conference on Neural Networks},


isbn = {9781509061815},
keywords = {Educational Data Mining (EDM),Massive Open Online Courses (MOOCs),Receiver operating characteristic (ROC)},
pages = {713--720},
title = {{Machine learning approaches to predict learning outcomes in Massive open online courses}},

volume = {2017-May},
year = {2017}
}
@article{Gisbrecht2015,
abstract = {Domain specific (dis-)similarity or proximity measures used e.g. in alignment algorithms of sequence data are popular to analyze complicated data objects and to cover domain specific data properties. Without an underlying vector space these data are given as pairwise (dis-)similarities only. The few available methods for such data focus widely on similarities and do not scale to large datasets. Kernel methods are very effective for metric similarity matrices, also at large scale, but costly transformations are necessary starting with non-metric (dis-) similarities. We propose an integrative combination of Nystr{\"{o}}m approximation, potential double centering and eigenvalue correction to obtain valid kernel matrices at linear costs in the number of samples. By the proposed approach effective kernel approaches become accessible. Experiments with several larger (dis-)similarity datasets show that the proposed method achieves much better runtime performance than the standard strategy while keeping competitive model accuracy. The main contribution is an efficient and accurate technique, to convert (potentially non-metric) large scale dissimilarity matrices into approximated positive semi-definite kernel matrices at linear costs.},
archivePrefix = {arXiv},
arxivId = {1411.1646},
author = {Gisbrecht, Andrej and Schleif, Frank Michael},

eprint = {1411.1646},

issn = {18728286},
journal = {Neurocomputing},
keywords = {Dissimilarity learning,Double centering,Indefinite kernel,Linear eigenvalue correction,Nystr{\"{o}}m approximation,Pseudo-Euclidean},
pages = {643--657},
publisher = {Elsevier},
title = {{Metric and non-metric proximity transformations at linear costs}},

volume = {167},
year = {2015}
}
@article{Long2014a,
abstract = {Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a unified way based on the structural risk minimization principle and the regularization theory. Specifically, ARTL learns the adaptive classifier by simultaneously optimizing the structural risk functional, the joint distribution matching between domains, and the manifold consistency underlying marginal distribution. Based on the framework, we propose two novel methods using Regularized Least Squares (RLS) and Support Vector Machines (SVMs), respectively, and use the Representer theorem in reproducing kernel Hilbert space to derive corresponding solutions. Comprehensive experiments verify that ARTL can significantly outperform state-of-the-art learning methods on several public text and image datasets. Copyright {\textcopyright} 2013 IEEE.},
archivePrefix = {arXiv},
arxivId = {PAI},
author = {Long, Mingsheng and Wang, Jianmin and Ding, Guiguang and Pan, Sinno Jialin and Yu, Philip S.},

eprint = {PAI},

isbn = {1041-4347},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Transfer learning,adaptation regularization,distribution adaptation,generalization error,manifold regularization},
number = {5},
pages = {1076--1089},
pmid = {8190083},
title = {{Adaptation regularization: A general framework for transfer learning}},
volume = {26},
year = {2014}
}
@article{DaSilva2017,
abstract = {{\textcopyright} 2017 IEEE. In many real-world applications, data arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. In data streams learning, classification problems aim to predict the true class of incoming instances in real time. While adhering to online learning strategies, in this paper we extend the On-Demand classification algorithm to include concepts of fuzzy sets theory as a way to make classification more flexible to stream changes. A set of experiments was conducted to evaluate the proposed method. Experiments show that our approach is promising in dealing with imbalanced data streams and presents benefits with relation to the non-fuzzy version.},
author = {{Da Silva}, Tiago Pinho and Urban, Gerson Ant{\^{o}}nio and Lopes, Priscilla De Abreu and Camargo, Heloisa De Arruda},


isbn = {9781538624074},
journal = {Proceedings - 2017 Brazilian Conference on Intelligent Systems, BRACIS 2017},
keywords = {Classification,Data Stream,Fuzzy},
pages = {67--72},
title = {{A fuzzy variant for on-demand data stream classification}},

volume = {2018-Janua},
year = {2018}
}
@article{shaffer1995multiple,
author = {Shaffer, Juliet Popper},

journal = {Annual review of psychology},
keywords = {closed test procedures,multiple comparisons,p-values,pairwise comparisons,simultaneous testing},
number = {1},
pages = {561--584},
publisher = {Annual Reviews 4139 El Camino Way, PO Box 10139, Palo Alto, CA 94303-0139, USA},
title = {{Multiple hypothesis testing}},
volume = {46},
year = {1995}
}
@article{Zou2019,
abstract = {We propose a novel domain adaptation framework, namely Consensus Adversarial Domain Adaptation (CADA), that gives freedom to both target encoder and source encoder to embed data from both domains into a common domaininvariant feature space until they achieve consensus during adversarial learning. In this manner, the domain discrepancy can be further minimized in the embedded space, yielding more generalizable representations. The framework is also extended to establish a new few-shot domain adaptation scheme (F-CADA), that remarkably enhances the ADA performance by efficiently propagating a few labeled data once available in the target domain. Extensive experiments are conducted on the task of digit recognition across multiple benchmark datasets and a real-world problem involving WiFi-enabled device-free gesture recognition under spatial dynamics. The results show the compelling performance of CADA versus the state-of-the-art unsupervised domain adaptation (UDA) and supervised domain adaptation (SDA) methods. Numerical experiments also demonstrate that F-CADA can significantly improve the adaptation performance even with sparsely labeled data in the target domain.},
author = {Zou, Han and Zhou, Yuxun and Yang, Jianfei and Liu, Huihan and Das, Hari Prasanna and Spanos, Costas J.},


issn = {2374-3468},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
month = {jul},
pages = {5997--6004},
title = {{Consensus Adversarial Domain Adaptation}},

volume = {33},
year = {2019}
}
@incollection{NIPS2016_6110,
author = {Long, Mingsheng and Zhu, Han and Wang, Jianmin and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {Lee, D D and Sugiyama, M and Luxburg, U V and Guyon, I and Garnett, R},

pages = {136--144},
publisher = {Curran Associates, Inc.},
title = {{Unsupervised Domain Adaptation with Residual Transfer Networks}},

year = {2016}
}
@article{Raykar2008,
author = {Raykar, V and Krishnapuram, B and Bi, J and Dundar, M and Rao, R},

journal = {Proceedings of the 25th Annual International Conference on Machine Learning (ICML 2008)},
pages = {808--815},
title = {{Bayesian multiple instance learning: automatic feature selection and inductive transfer}},
year = {2008}
}
@article{Frenay2017,
author = {Frenay, Benoit and Hammer, Barbara},


isbn = {9781509061815},
journal = {Proceedings of the International Joint Conference on Neural Networks},
pages = {1748--1755},
title = {{Label-noise-tolerant classification for streaming data}},
volume = {2017-May},
year = {2017}
}
@inproceedings{Li2015,
abstract = {We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample via a single feedforward pass through a multilayer preceptron, as in the recently proposed generative adversarial networks (Goodfellow et al., 2014). Training a generative adversarial network, however, requires careful optimization of a difficult minimax program. Instead, we utilize a technique from statistical hypothesis testing known as maximum mean discrepancy (MMD), which leads to a simple objective that can be interpreted as matching all orders of statistics between a dataset and samples from the model, and can be trained by backpropagation. We further boost the performance of this approach by combining our generative network with an auto-encoder network, using MMD to learn to generate codes that can then be decoded to produce samples. We show that the combination of these techniques yields excellent generative models compared to baseline approaches as measured on MNIST and the Toronto Face Database.},
address = {Lille, France},
author = {Li, Yujia and Swersky, Kevin and Zemel, Rich},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
editor = {Bach, Francis and Blei, David},

pages = {1718--1727},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Generative Moment Matching Networks}},

volume = {37},
year = {2015}
}
@inproceedings{Yamaguchi2019,
abstract = {There is a need for forecasting of short-range future values in data streams such as traffic flows, stock prices, and electricity consumption. However, concept drift in non-stationary data streams is an important problem. We propose an online prediction method called OPOSSAM for such data streams. OPOSSAM manages time-series segments in short-term memory and long-term memory, and forecasts future values by local regression based on the similarity of time-series segments. In particular, OPOSSAM keeps long-term memory consistent by reducing redundant samples with large prediction errors, and automatically adjusts the prediction model based on short-term memory from the prior model learned from the entire memory in order to deal with concept drift. Experimental results show accuracy superior to that of baseline methods on real-world datasets of traffic flow, stock prices, and electricity consumption.},
author = {Yamaguchi, Akihiro and Maya, Shigeru and Inagi, Tatsuya and Ueno, Ken},
booktitle = {2018 IEEE International Conference on Big Data (Big Data)},


isbn = {978-1-5386-5035-6},
keywords = {Biased L2-regularization,Concept drift,Data stream,Online prediction,Short-range forecasting},
month = {dec},
pages = {2355--2364},
publisher = {IEEE},
title = {{OPOSSAM: Online Prediction of Stream Data Using Self-adaptive Memory}},

year = {2018}
}
@article{Ditzler2012,
abstract = {Learning in nonstationary environments, also known as learning concept drift, is concerned with learning from data whose statistical characteristics change over time. Concept drift is further complicated if the data set is class imbalanced. While these two issues have been independently addressed, their joint treatment has been mostly underexplored. We describe two ensemble-based approaches for learning concept drift from imbalanced data. Our first approach is a logical combination of our previously introduced Learn++.NSE algorithm for concept drift, with the well-established SMOTE for learning from imbalanced data. Our second approach makes two major modifications to Learn++.NSE-SMOTE integration by replacing SMOTE with a subensemble that makes strategic use of minority class data; and replacing Learn++.NSE and its class-independent error weighting mechanism with a penalty constraint that forces the algorithm to balance accuracy on all classes. The primary novelty of this approach is in determining the voting weights for combining ensemble members, based on each classifier's time and imbalance-adjusted accuracy on current and past environments. Favorable results in comparison to other approaches indicate that both approaches are able to address this challenging problem, each with its own specific areas of strength. We also release all experimental data as a resource and benchmark for future research. {\textcopyright} 1989-2012 IEEE.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Ditzler, Gregory and Polikar, Robi},

eprint = {arXiv:1011.1669v3},

isbn = {1041-4347},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Incremental learning,class imbalance,concept drift,multiple classifier systems},
number = {10},
pages = {2283--2301},
pmid = {25246403},
title = {{Incremental learning of concept drift from streaming imbalanced data}},
volume = {25},
year = {2013}
}
@article{Paassen2016,
abstract = {Typical intelligent tutoring systems rely on detailed domain-knowledge which is hard to obtain and difficult to encode. As a data-driven alternative to explicit domain-knowledge, one can present learners with feedback based on similar existing solutions from a set of stored examples. At the heart of such a data-driven approach is the notion of similarity. We present a general-purpose framework to construct structure metrics on sequential data and to adapt those metrics using machine learning techniques. We demonstrate that metric adaptation improves the classification of wrong versus correct learner attempts in a simulated data set from sports training, and the classification of the underlying learner strategy in a real Java programming dataset.},
author = {Paassen, Benjamin and Mokbel, Bassam and Hammer, Barbara},


isbn = {9782875870148},
issn = {18728286},
journal = {Neurocomputing},
keywords = {Algebraic dynamic programming,Intelligent tutoring systems,Learning vector quantization,Metric learning,Sequential data},
pages = {3--13},
publisher = {Elsevier},
title = {{Adaptive structure metrics for automated feedback provision in intelligent tutoring systems}},

volume = {192},
year = {2016}
}
@inproceedings{Wang.2016,
abstract = {Data streams classification poses three major challenges, namely, infinite length, concept-drift, and featureevolution. The first two issues have been widely studied. However, most existing data stream classification techniques ignore the last one. DXMiner [17], the first model which addresses featureevolution by using the past labeled instances to select the top ranked features based on a scores computed by a formula. This semi-supervised feature selection method depends on the quality of the past classification and neglects the possible correlation among different features, thus unable to produce an optimal feature subset which deteriorates the accuracy of classification. Multi-Cluster Feature Selection (MCFS) [5] proposed for static data classification and clustering applies unsupervised feature selection to address the feature-evolution problem, but suffers from the high computational cost in feature selection. In this paper, we apply MCFS in the DXMiner framework to handle each window of data in a data stream for dynamic data stream-classification. With unsupervised feature selection, our method produces the optimal feature subset and hence improves DXMiner on the classification accuracy. We further improve the time complexity of the feature selection process in MCFS by using the locality sensitive hashing forest (LSH Forest) [4]. The empirical results indicate that our approach outperforms stateof- the-art streams classification techniques in classifying real-life data streams.},
author = {Wang, Lulu and Shen, Hong},
booktitle = {Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings},


isbn = {9781509050819},
pages = {221--226},
title = {{Improved data streams classification with fast unsupervised feature selection}},

year = {2017}
}
@article{Paige1986,


issn = {0196-5204},
journal = {SIAM Journal on Scientific and Statistical Computing},
keywords = {discrete mathematics,generalized singular value decomposition,iterative method,mathematical analysis,mathematical optimization,mathematics,matrix,matrix decomposition,parallel algorithm,singular value,systolic array,unitary matrix},
month = {oct},
number = {4},
pages = {1126--1146},
title = {{Computing the Generalized Singular Value Decomposition}},

volume = {7},
year = {1986}
}
@inproceedings{Vural2018,
abstract = {Many domain adaptation methods are based on learning a projection or a transformation of the source and target domains to a common domain and training a classifier there, while the performance of such algorithms has not been theoretically studied yet. Previous studies proposing generalization bounds for domain adaptation relate the target loss to the discrepancy between the source and target distributions, however, do not take into account the possible effects of learning a transformation between the two domains. In this work, we present generalization bounds that study the target performance of domain adaptation methods learning a transformation of the source and target domains along with a hypothesis. We show that, under some conditions on the loss regularity, if the domain transformations reduce the distribution distance at a sufficiently high rate, then the expected target loss can be bounded with probability improving at an exponential rate with the number of labeled samples. {\textcopyright} 2018 IEEE.},
address = {Dept. of Electrical and Electronics Engineering, Middle East Technical University, Turkey},
author = {Vural, Elif},
booktitle = {2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)},

editor = {N., Pustelnik and Z.-H., Tan and Z., Ma and J., Larsen},

isbn = {978-1-5386-5477-4},
keywords = {Artificial intelligence,Domain adaptation,Domain transformation,Domain transformations,Exponential rates,Generalization bound,Generalization bounds,High rate,Learning systems,Performance analysis,Probability distributions,Signal processing,Target domain,Two domains},
language = {English},
month = {sep},
pages = {1--6},
publisher = {IEEE},
title = {{GENERALIZATION BOUNDS FOR DOMAIN ADAPTATION VIA DOMAIN TRANSFORMATIONS}},

volume = {2018-Septe},
year = {2018}
}
@article{Peng2019,
author = {Peng, Zhihao and Zhang, Wei and Han, Na and Fang, Xiaozhao and Kang, Peipei and Teng, Luyao},


isbn = {2018040103},
issn = {1051-8215},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
number = {February},
pages = {1--1},
publisher = {IEEE},
title = {{Active Transfer Learning}},

volume = {PP},
year = {2019}
}
@inproceedings{Ghifary2015,
author = {Ghifary, Muhammad and Kleijn, W Bastiaan and Zhang, Mengjie and Balduzzi, David},
booktitle = {2015 IEEE International Conference on Computer Vision (ICCV)},


isbn = {978-1-4673-8391-2},
month = {dec},
pages = {2551--2559},
publisher = {IEEE},
title = {{Domain Generalization for Object Recognition with Multi-task Autoencoders}},

year = {2015}
}
@incollection{NIPS2019_8420,
author = {Rabanser, Stephan and G{\"{u}}nnemann, Stephan and Lipton, Zachary},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {1396--1408},
publisher = {Curran Associates, Inc.},
title = {{Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift}},

year = {2019}
}
@article{Cruz-Vega2017,
abstract = {{\textcopyright} 2016, Springer-Verlag Berlin Heidelberg.In supervised classification, learning vector quantization (LVQ) methods are commonly used due to their intuitive structure based on prototypical instances that reduce considerably the computations in the classification process. Several improvements of LVQ have been proposed based on heuristics including LVQ3, and GLVQ. All these methods use the Euclidean distance to evaluate the similarity between prototypes and objects, which may be inappropriate if features are not equally scaled. Metric adaption techniques try to alleviate this problem by learning discriminative distance measures from the training data. Generalized relevance learning vector quantization is one of such improvements. However, in big data problems LVQ algorithms require incremental learning mechanisms. This paper introduces an LVQ-algorithm based on granular computing for prototye-based classification equipped with incremental learning mechanisms. The proposed algorithm is able to group entities with similar features, and at the same time proposes new prototypes to better cover the class distribution with prototyping elements. Two steps for the automatic control of prototypes are proposed: the first one controls the number of prototypes by a usage-frequency indicator; whereas the second one, is designed to learn the relevance of data dimensions, producing an automatic pruning of useless dimensions, avoiding a high computational load and increasing the interpretability of the resulting model. The proposed method is evaluated in benchmark data and obtains competitive performance with state-of-the-art solutions. In the case of big data sets, we obtained the best accuracy rate of about 72 {\%} with a good compression rate of around 94 {\%}.},
author = {Cruz-Vega, Israel and Escalante, Hugo Jair},


isbn = {0050001620},
issn = {14337479},
journal = {Soft Computing},
keywords = {Big data,GRLVQ,Incremental,Online},
number = {14},
pages = {3931--3944},
publisher = {Springer Berlin Heidelberg},
title = {{An online and incremental GRLVQ algorithm for prototype generation based on granular computing}},
volume = {21},
year = {2017}
}
@article{Zhang2016a,
abstract = {{\textcopyright} 1992-2012 IEEE. We propose a novel reconstruction-based transfer learning method called latent sparse domain transfer (LSDT) for domain adaptation and visual categorization of heterogeneous data. For handling cross-domain distribution mismatch, we advocate reconstructing the target domain data with the combined source and target domain data points based on ℓ 1  -norm sparse coding. Furthermore, we propose a joint learning model for simultaneous optimization of the sparse coding and the optimal subspace representation. In addition, we generalize the proposed LSDT model into a kernel-based linear/nonlinear basis transformation learning framework for tackling nonlinear subspace shifts in reproduced kernel Hilbert space. The proposed methods have three advantages: 1) the latent space and the reconstruction are jointly learned for pursuit of an optimal subspace transfer; 2) with the theory of sparse subspace clustering, a few valuable source and target data points are formulated to reconstruct the target data with noise (outliers) from source domain removed during domain adaptation, such that the robustness is guaranteed; and 3) a nonlinear projection of some latent space with kernel is easily generalized for dealing with highly nonlinear domain shift (e.g., face poses). Extensive experiments on several benchmark vision data sets demonstrate that the proposed approaches outperform other state-of-the-art representation-based domain adaptation methods.},
author = {Zhang, Lei and Zuo, Wangmeng and Zhang, David},


issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Transfer learning,domain adaptation,heterogeneous data,visual categorization},
number = {3},
pages = {1177--1191},
publisher = {IEEE},
title = {{LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation}},
volume = {25},
year = {2016}
}
@article{SUN2016,
abstract = {To overcome the drawback of using supervised learning to extract fault features for classification in most of current induction motor fault diagnosis approaches, a deep neural network algorithm is presented, which is realized by the sparse auto-encoder combined with the denoising auto-encoder, to achieve unsupervised feature learning for fault diagnosis of induction motors. Sparse auto-encoder can learn the inherent features and extract the succinct expressions from complex data automatically. In addition, the method of denoising auto-encoder can increase the robustness of feature expression, thus improving the performance of the sparse auto-encoder. The extracted features can then be used to train a neural network classifier and complete the deep neural network construction. The back-propagation algorithm is used for fine-tuning the deep neural network with the purpose of improving the accuracy of fault classification. The "dropout" technique is also introduced into to the entire training process to reduce the prediction error caused by "overfitting". Experimental results have shown that, compared with the traditional back propagation (BP) neural network, the presented deep neural network can realize induction motor fault diagnosis more effectively. {\textcopyright} 2016 Journal of Mechanical Engineering.},
author = {SUN, Wenjun},


issn = {0577-6686},
journal = {Journal of Mechanical Engineering},
keywords = {Machine Learning},
number = {9},
pages = {65},
title = {{Induction Motor Fault Diagnosis Based on Deep Neural Network of Sparse Auto-encoder}},
volume = {52},
year = {2016}
}
@incollection{NIPS2006_2983,
annote = {A - Distance: Maximum difference in probability of all subsets A on a given distribution D and D'.},
author = {Ben-David, Shai and Blitzer, John and Crammer, Koby and Pereira, Fernando},
booktitle = {Advances in Neural Information Processing Systems 19},
editor = {Sch{\"{o}}lkopf, B and Platt, J C and Hoffman, T},

pages = {137--144},
publisher = {MIT Press},
title = {{Analysis of Representations for Domain Adaptation}},

year = {2007}
}
@inproceedings{Peng2020Federated,
author = {Peng, Xingchao and Huang, Zijun and Zhu, Yizhe and Saenko, Kate},
booktitle = {International Conference on Learning Representations},

title = {{Federated Adversarial Domain Adaptation}},

year = {2020}
}
@inproceedings{Chen_2018_CVPR,
author = {Chen, Qingchao and Liu, Yang and Wang, Zhaowen and Wassell, Ian and Chetty, Kevin},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation}},
year = {2018}
}
@inproceedings{Gama.2005,
abstract = {This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental algorithm, that works online, processing each example in constant time, and performing a single scan over the train- ing examples. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. For multi-class prob- lems the algorithm grows a binary tree for each possible pair of classes, leading to a forest of trees. Decision nodes and leaves contain naive-Bayes classifiers playing different roles during the induction process. Naive-Bayes in leaves are used to classify test examples, naive-Bayes in inner nodes can be used as multivariate splitting-tests if chosen by the splitting criteria, and used to detect drift in the distribution of the ex- amples that traverse the node. When a drift is detected, all the sub-tree rooted at that node will be pruned. The use of naive-Bayes classifiers at leaves to classify test examples, the use of splitting-tests based on the outcome of naive-Bayes, and the use of naive-Bayes classifiers at decision nodes to detect drift are directly obtained from the sufficient statistics required to compute the splitting criteria, without no additional computations. This aspect is a main advantage in the context of high-speed data streams. This methodol- ogy was tested with artificial and real-world data sets. The experimental results show a very good performance in com- parison to a batch decision tree learner, and high capacity to detect and react to drift.},
author = {Gama, Jo{\~{a}}o and Medas, Pedro and Rodrigues, Pedro},
booktitle = {Proceedings of the 2005 {\{}ACM{\}} Symposium on Applied Computing (SAC), Santa Fe, New Mexico, USA, March 13-17, 2005},


pages = {573},
title = {{Learning decision trees from dynamic data streams}},

year = {2005}
}
@inproceedings{office31,
abstract = {Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to non-image data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.},
address = {Berlin, Heidelberg},
author = {Saenko, Kate and Kulis, Brian and Fritz, Mario and Darrell, Trevor},
booktitle = {Computer Vision -- ECCV 2010},
editor = {Daniilidis, Kostas and Maragos, Petros and Paragios, Nikos},

isbn = {978-3-642-15561-1},
pages = {213--226},
publisher = {Springer Berlin Heidelberg},
title = {{Adapting Visual Category Models to New Domains}},
year = {2010}
}
@inproceedings{DBLP:conf/nips/LongC0J18,
annote = {CDAN extends DANN
ImageCLEF-DA

For DANN Let F(x) be feature extractor, G(F(x)) be classifier and D(G(X)) be domain classifer.


Two extensions to DANN:
1.CDANN defines the input of D(x) as multilinear map h = f x g that the the features f are conditioned with the classifier predictions. This conditions is vital as it captures the cross-variance between features and predictions. The predictions are one hot vectors and by definition of tensor multiplication and the expected value it gives the conditional expected value of f given a label prediction.

Expected usecase: By Multilinear Map network can learn complex multimodal distributions because predictions can reveal this multimodality and the feature output can be conditioned with it.

2. Entropy weighting for predictions to module a weighting factor in the mulitlinear map for computing the final loss.

Expected usecase:
Lower relevance of hard to transfer examples which deterioreate the procedure and improve the procedure towards safe transfer:
Hard to transfer examples have low certainty.
Authors extend the DANN generalization error to CDAN},
author = {Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montr{\'{e}}al, Canada},

pages = {1647--1657},
title = {{Conditional Adversarial Domain Adaptation}},

year = {2018}
}
@article{Callaghan2002,
abstract = {Streaming data analysis has recently attracted attention$\backslash$r$\backslash$nin numerous applications including telephone$\backslash$r$\backslash$nrecords, web documents and clickstreams. For such$\backslash$r$\backslash$nanalysis, single-pass algorithms that consume a smal l$\backslash$r$\backslash$namount of memory are critical. We describe such a$\backslash$r$\backslash$nstreaming algorithm that eectively clusters large data$\backslash$r$\backslash$nstreams. We also provide empirical evidence of the$\backslash$r$\backslash$nalgorithm's performance on synthetic and real data$\backslash$r$\backslash$nstreams},
author = {O'Callaghan, L. and Mishra, N. and Meyerson, A. and Guha, S. and Motwani, R.},


isbn = {0-7695-1531-2},
issn = {1063-6382},
journal = {18th International Conference on Data Engineering (ICDE'02)},
pages = {685--694},
title = {{Streaming-data algorithms for high-quality clustering}},
year = {2003}
}
@article{Liu2019,
author = {Liu, Youfa and Tu, Weiping and Du, Bo and Zhang, Lefei and Tao, Dacheng},


issn = {1057-7149},
journal = {IEEE Transactions on Image Processing},
number = {c},
pages = {1074--1089},
title = {{Homologous Component Analysis for Domain Adaptation}},

volume = {29},
year = {2020}
}
@article{Sato,
author = {Sato, Ryo and Kawashima, Hideyuki and Engineering, Information and Kitagawa, Hiroyuki},

journal = {Ninth International Conference on Mobile Data Management Workshops, MDMW},
pages = {114--121},
title = {{The Integration of Data Streams with Probabilities Ryo Sato}},
year = {2008}
}
@incollection{NIPS2000_1866,
abstract = {A major problem for kernel-based predictors (such as Support Vector Machines and Gaussian processes) is that the amount of computation required to find the solution scales as O(n ), where n is the number of training examples. We show that an approximation to the eigendecomposition of the Gram matrix can be computed by the Nystr{\"{o}}m method (which is used for the numerical solution of eigenproblems). This is achieved by carrying out an eigendecomposition on a smaller system of size m {\textless} n, and then expanding the results back up to n dimensions. The computational complexity of a predictor using this approximation is O(m n). We report experiments on the USPS and abalone data sets and show that we can set m n without any significant decrease in the accuracy of the solution.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Williams, Christopher and Seeger, Matthias W.},
booktitle = {NIPS Proceedings},

editor = {Leen, T K and Dietterich, T G and Tresp, V},
eprint = {arXiv:1011.1669v3},

isbn = {9788578110796},
issn = {1098-6596},
keywords = {icle},
pages = {682--688},
pmid = {25246403},
publisher = {MIT Press},
title = {{Using the Nystrom Method to Speed Up Kernel Machines}},
volume = {13},
year = {2001}
}
@inproceedings{Rakshit2019,
abstract = {In unsupervised deep domain adaptation (DA), the use of adversarial domain classifiers is popular in learning a shared feature space which reduces the distributions gap for a pair of source (with training data) and target (with only test data) domains. In the new space, a classifier trained on source training data is expected to generalize well for the target domain samples. We hypothesize that such a feature space obtained by aligning the domains globally ignores the category level feature distributions. This, in turn, leads to erroneous mapping for fine-grained classes. Besides, the discriminative-ness of the shared space is not explicitly addressed. In order to resolve both the issues, we propose a novel adver-sarial approach which judiciously refines the space learned by the domain classifier by incorporating class level information. We follow an ensemble classifiers based approach to model the source domain and introduce a novel consistency constrain on the classifier's outcomes when evaluated on a held-out set of target domain samples. We further leverage the ensemble learning strategy during the inference , as opposed to the existing single classifier based methods. We find that our deep DA model is capable of producing a compact and better domain aligned feature space. Experimental results obtained on the Office-Home, Office-CalTech, MNIST-USPS, and a remote sensing dataset confirm the superiority of the proposed approach.},
author = {Rakshit, Sayan and Chaudhuri, Ushasi and Banerjee, Biplab and Chaudhuri, Subhasis},
booktitle = {2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},


isbn = {978-1-7281-2506-0},
month = {jun},
pages = {657--666},
publisher = {IEEE},
title = {{Class Consistency Driven Unsupervised Deep Adversarial Domain Adaptation}},

year = {2019}
}
@article{Fang2015,
abstract = {Multi-source transfer learning focuses on studying the scarcity of samples with labels in target domain, while neglecting the analysis about transferability relationship among multiple source domains. Thus, we propose a method that transforms samples in target domain into multi-label samples, with which it is able to analyze the correlations among predicted labels from different sources. We design a method that can extract the shared subspace among labels in multi-sources, and propose a novel multi-source transfer learning method based on multi-label shared subspace. This approach is required when knowledge about multiple sources are available but it is unknown which source is of more transferability. Experiments show that our proposed algorithm can improve the performance of transfer learning method and alleviate time complexity.},
author = {Fang, Min and Guo, Yong and Zhang, Xiaosong and Li, Xiao},


issn = {01678655},
journal = {Pattern Recognition Letters},
keywords = {Multi-source,Shared subspace of labels,Transfer learning},
pages = {101--106},
title = {{Multi-source transfer learning based on label shared subspace}},
volume = {51},
year = {2015}
}
@inproceedings{pmlr-v80-xie18c,
abstract = {It is important to transfer the knowledge from label-rich source domain to unlabeled target domain due to the expensive cost of manual labeling efforts. Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior methods is that they ignore the semantic information contained in samples, e.g., features of backpacks in target domain might be mapped near features of cars in source domain. In this paper, we present moving semantic transfer network, which learn semantic representations for unlabeled target samples by aligning labeled source centroid and pseudo-labeled target centroid. Features in same class but different domains are expected to be mapped nearby, resulting in an improved target classification accuracy. Moving average centroid alignment is cautiously designed to compensate the insufficient categorical information within each mini batch. Experiments testify that our model yields state of the art results on standard datasets.},
address = {Stockholmsm{\"{a}}ssan, Stockholm Sweden},
author = {Xie, Shaoan and Zheng, Zibin and Chen, Liang and Chen, Chuan},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
editor = {Dy, Jennifer and Krause, Andreas},

pages = {5423--5432},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Learning Semantic Representations for Unsupervised Domain Adaptation}},

volume = {80},
year = {2018}
}
@inproceedings{Wankhade.2013,
abstract = {The increasing use of technology in diverse field has caused generation of huge volumes of information streams. Data streams contains bulk of data points generated at high speed continuously from various applications like log records, web clicks etc. With recent advancement in technology need for analysis of such unbounded streams is increasing day by day. Data mining process helps to excavate useful knowledge from rapidly generated raw data streams. In context with the continuously generated data, mining data streams is emerging challenging task in which several issues like limited space, limited time, accuracy, handling evolving data need to be considered. This paper provides an overview of various approaches for handling changing and evolving data streams. {\textcopyright} 2013 IEEE.},
author = {Wankhade, Kapil and Hasan, Tasneem and Thool, Ravindra},
booktitle = {Proceedings - 2013 International Conference on Communication Systems and Network Technologies, CSNT 2013},


keywords = {Classification,Clustering,Concept drift,Data mining,Data streams},
month = {apr},
pages = {621--625},
title = {{A survey: Approaches for handling evolving data streams}},
year = {2013}
}
@article{Alonso2005,
abstract = {Web-based e-learning education research and development now focuses on the inclusion of new technological features and the exploration of software standards. However, far less effort is going into finding solutions to psychopedagogical problems in this new educational category. This paper proposes a psychopedagogical instructional model based on content structure, the latest research into information processing psychology and social contructivism, and defines a blended approach to the learning process. Technologically speaking, the instructional model is supported by learning objects, a concept inherited from the object-oriented paradigm.},
author = {Alonso, Fernando and L{\'{o}}pez, Genoveva and Manrique, Daniel and Vi{\~{n}}es, Jos{\'{e}} M.},


isbn = {0007-1013},
issn = {00071013},
journal = {British Journal of Educational Technology},
number = {2},
pages = {217--235},
pmid = {16224271},
title = {{An instructional model for web-based e-learning education with a blended learning process approach}},
volume = {36},
year = {2005}
}
@article{Mancini2018,
abstract = {The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains. Alleviating the domain shift problem, especially in the challenging setting where no labeled data are available for the target domain, is paramount for having visual recognition systems working in the wild. As the problem stems from a shift among distributions, intuitively one should try to align them. In the literature, this has resulted in a stream of works attempting to align the feature representations learned from the source and target domains. Here we take a different route. Rather than introducing regularization terms aiming to promote the alignment of the two representations, we act at the distribution level through the introduction of $\backslash$emph{\{}DomaIn Alignment Layers{\}} ($\backslash$DIAL), able to match the observed source and target data distributions to a reference one. Thorough experiments on three different public benchmarks we confirm the power of our approach.},
archivePrefix = {arXiv},
arxivId = {arXiv:1702.06332},
author = {Carlucci, Fabio Maria and Porzi, Lorenzo and Caputo, Barbara and Ricci, Elisa and Bul{\`{o}}, Samuel Rota},

eprint = {arXiv:1702.06332},

isbn = {9783319685595},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Deep models,Entropy loss,Feature normalization,Unsupervised domain adaptation},
pages = {357--369},
title = {{Just DIAL: Domain alignment layers for unsupervised domain adaptation}},

volume = {10484 LNCS},
year = {2017}
}
@incollection{NIPS2018_8075,
author = {Zhao, Han and Zhang, Shanghang and Wu, Guanhang and Moura, Jos{\'{e}} M F and Costeira, Joao P and Gordon, Geoffrey J},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R},

pages = {8559--8570},
publisher = {Curran Associates, Inc.},
title = {{Adversarial Multiple Source Domain Adaptation}},

year = {2018}
}
@article{Hembury2003,
abstract = {It was found that FeD adopted a stable syn conformation as a result of the inclusion of atomic oxygen in a $\mu$-oxo fashion, but on exposure to acidic conditions converted into the extended anti form. When this was subsequently made basic the syn conformation was re-adopted, simply due to re-inclusion of atomic oxygen forming the $\mu$-oxo species. It was found that the combination of these affects allowed FeD to undergo repeated conformational switching cycles that were reproducible and robust.},
author = {Hembury, Guy A. and Borovkov, Victor V. and Lintuluoto, Juha M. and Inoue, Yoshihisa},


issn = {0366-7022},
journal = {Chemistry Letters},
number = {5},
pages = {428--429},
title = {{ An Acid–Base Controlled Molecular Switch. syn – anti Conformational Switching in a $\mu$-oxo Bis(Iron Porphyrin) }},
volume = {32},
year = {2003}
}
@article{Bodyanskiy2016,
abstract = {In the paper, the deep evolving neural network and its learning algorithms (in batch and on-line mode) are proposed. The deep evolving neural network's architecture is developed based on Group Method of Data Handling approach and Least Squares Support Vector Machines with fixed number of the synaptic weights. The proposed system is simple in computational implementation, characterized by high learning speed and allows processing of data, which are fed sequentially in on-line mode. The proposed system can be used for solving a wide class of Dynamic Data Mining and Data Stream Mining tasks, which are connected with non-stationary, nonlinear stochastic and chaotic signals. The computational experiments are confirmed to effectiveness of the developed approach.},
author = {Bodyanskiy, Y and Vynokurova, O and Pliss, I and Setlak, G and Mulesa, P},


isbn = {9781509037360},
issn = {1045-9227},
journal = {2016 IEEE First International Conference on Data Stream Mining Processing (DSMP)},
keywords = {Biological neural networks,Computer architecture,Data mining,GMDH,LS-SVM,Neurons,Support vector machines,Training,Tuning,batch learning algorithm,chaotic signals,computational intelligence,data mining,data processing,data stream mining,data stream mining tasks,deep evolving GMDH-SVM neural network,deep evolving neural network architecture,deep learning,dynamic data mining,forecasting theory,group method of data handling,identification,learning (artificial intelligence),learning speed,least squares approximations,least squares support vector machines,neural net architecture,nonlinear stochastic signals,nonstationary signals,online mode learning algorithm,support vector machines,synaptic weights},
number = {August},
pages = {257--262},
title = {{Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks}},
year = {2016}
}
@article{Pan2011,
abstract = {Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.},
author = {Pan, Sinno Jialin and Tsang, Ivor W. and Kwok, James T. and Yang, Qiang},


isbn = {9781577354260},
issn = {10459227},
journal = {IEEE Transactions on Neural Networks},
keywords = {Dimensionality reduction,Hilbert space embedding of distributions,domain adaptation,transfer learning},
number = {2},
pages = {199--210},
pmid = {21095864},
title = {{Domain adaptation via transfer component analysis}},
volume = {22},
year = {2011}
}
@inproceedings{Domingos.2000,
abstract = {Many organizations today have more than very large data- bases; they have databases that grow without limit at a rate of several million records per day. Mining these con- tinuous data streams brings unique opportunities, but also new challenges. This paper describes and evaluates VFDT, an anytime system that builds decision trees using constant memory and constant time per example. VFDT can in- corporate tens of thousands of examples per second using o -the-shelf hardware. It uses Hoe ding bounds to guar- antee that its output is asymptotically nearly identical to that of a conventional learner. We study VFDT's proper- ties and demonstrate its utility through an extensive set of experiments on synthetic data. We apply VFDT to mining the continuous stream of Web access data from the whole University of Washington main campus.},
author = {Domingos, Pedro and Hulten, Geoff},
booktitle = {Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining  - KDD '00},


isbn = {1581132336},
pages = {71--80},
title = {{Mining high-speed data streams}},

year = {2000}
}
@article{Wang2016,
abstract = {The National Synchrotron Light Source II (NSLS-II) at Brookhaven
National Laboratory (BNL) is now providing some of the world's brightest
x-ray beams. A suite of imaging and diffraction methods, exploiting
megapixel detectors with kilohertz frame-rates at NSLS-II beamlines,
generate a variety of image streams in unprecedented velocities and
volumes. A complete understanding of a complex material system often
requires a cluster of x-ray characterization tools that can reveal its
elemental, structural, chemical and physical properties at different
length-scales and time -scales. The flourish and continuing refinement
of x-ray probes enable that the same sample may be studied with
different perspectives and granularities, and at different time and
locations; these powerful tools generate a correspondingly daunting big
data challenge, with multiple image streams that outpaces any manual
efforts and traditional data analysis practice. In this paper, we
applied deep learning methods, in particular, deep convolutional neural
network (CNN) to automatically recognize image features from image
streams from NSLS-II, and integrated our deep-learning methods into the
Google Tensortlow to cluster and label both real and synthetic 2-D
scattering image patterns. These methods would empower scientists by
providing timely insights, allowing them to steer experiments
efficiently during their precious x-ray beamtime allocation. Experiment
shows that the CNN-based image labeling attains a 10{\%} improvement over
traditional K-mean and Support Vector Machine.},
author = {Wang, Boyu and Guan, Ziqiao and Yao, Shun and Qin, Hong and Nguyen, Minh Hoai and Yager, Kevin and Yu, Dantong},


isbn = {9781467390514},
journal = {2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings},
keywords = {CNN,Deep learning,X Ray Image Classification},
title = {{Deep learning for analysing synchrotron data streams}},
year = {2016}
}
@inproceedings{Cicek_2019_ICCV,
author = {Cicek, Safa and Soatto, Stefano},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},

title = {{Unsupervised Domain Adaptation via Regularized Conditional Alignment}},
year = {2019}
}
@article{Do2006,
abstract = {Linear text classification algorithms work by computing an inner product between a test document vector and a parameter vector. In many such algorithms, including naive Bayes and most TFIDF variants, the parameters are determined by some simple, closed-form, function of training set $\backslash$nstatistics; we call this mapping mapping from statistics to parameters, the $\backslash$emph{\{}parameter function{\}}. Much research in text classification over the last few decades has consisted of manual efforts to identify better parameter functions. In this paper, we propose an algorithm for automatically $\backslash$emph{\{}learning{\}} this function from related classification problems. The parameter function found by our algorithm then defines a $\backslash$emph{\{}new learning algorithm{\}} for text classification, which we can apply to novel classification tasks. We find that our learned classifier outperforms existing methods on a variety of multiclass text classification tasks. },
author = {Do, Chuong B and Ng, Andrew Y},


isbn = {9780262232531},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {3},
pages = {299--306},
title = {{Transfer learning for text classification}},
volume = {18},
year = {2006}
}
@article{Chen2016,
abstract = {{\textcopyright} 2016 IEEE. The application of current drift detection methods to real data streams show trends in the rate of change found by the detectors. We observe that these patterns of change vary across different data streams. We use the term stream volatility pattern to describe change rates with a distinct mean and variance. First, we propose a novel drift prediction algorithm to predict the location of future drift points based on historical drift trends which we model as transitions between stream volatility patterns. Our method uses a probabilistic network to learn drift trends and is independent of the drift detection technique. We demonstrate that our method is able to learn and predict drift trends in streams with reoccurring stream volatility patterns. This allows the anticipation of future changes which enables users and detection methods to be more proactive. Second, we apply our drift prediction algorithm by incorporating the drift estimates into a drift detector, ProSeed, to improve its performance by decreasing the false positive rate.},
author = {Chen, Kylie and Koh, Yun Sing and Riddle, Patricia},


isbn = {9781509006199},
journal = {Proceedings of the International Joint Conference on Neural Networks},
pages = {780--787},
title = {{Proactive drift detection: Predicting concept drifts in data streams using probabilistic networks}},
volume = {2016-Octob},
year = {2016}
}
@inproceedings{Pan:1999:CME:301250.301389,
address = {New York, NY, USA},
author = {Pan, Victor Y. and Chen, Zhao Q.},
booktitle = {Proceedings of the Thirty-first Annual ACM Symposium on Theory of Computing},


isbn = {1-58113-067-8},
pages = {507--516},
publisher = {ACM},
series = {STOC '99},
title = {{The complexity of the matrix eigenproblem}},

year = {2003}
}
@article{Raina2007,
abstract = {We present a new machine learning frame- work called “self-taught learning” for using unlabeled data in supervised classification tasks. We do not assume that the unlabeled data follows the same class labels or generative distribution as the labeled data. Thus, we would like to use a large number of unlabeled images (or audio samples, or text documents) randomly downloaded from the Internet to improve performance on a given image (or audio, or text) classification task. Such unlabeled data is significantly easier to obtain than in typical semi-supervised or transfer learning settings, making self- taught learning widely applicable to many practical learning problems. We describe an approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data. These features form a succinct input representation and significantly improve classification per- formance. When using an SVM for classification, we further show how a Fisher kernel can be learned for this representation.},
archivePrefix = {arXiv},
arxivId = {1403.6382},
author = {Raina, Rajat and Ng, Andrew Y},

eprint = {1403.6382},

isbn = {9781595937933},
issn = {01333852},
journal = {Proceedings of the 24th international conference on Machine learning},
pages = {759--766},
pmid = {87882338},
title = {{Self-taught Learning : Transfer Learning from Unlabeled Data}},
year = {2007}
}
@article{Hoffman2012,
abstract = {Methods of transfer learning try to combine knowledge from several related tasks (or domains) to improve performance on a test task. Inspired by causal methodology, we relax the usual covariate shift assumption and assume that it holds true for a subset of predictor variables: the conditional distribution of the target variable given this subset of predictors is invariant over all tasks. We show how this assumption can be motivated from ideas in the field of causality. We focus on the problem of Domain Generalization, in which no examples from the test task are observed. We prove that in an adversarial setting using this subset for prediction is optimal in Domain Generalization; we further provide examples, in which the tasks are sufficiently diverse and the estimator therefore outperforms pooling the data, even on average. If examples from the test task are available, we also provide a method to transfer knowledge from the training tasks and exploit all available features for prediction. However, we provide no guarantees for this method. We introduce a practical method which allows for automatic inference of the above subset and provide corresponding code. We present results on synthetic data sets and a gene deletion data set.},
archivePrefix = {arXiv},
arxivId = {1507.05333},
author = {Rojas-Carulla, Mateo and Sch{\"{o}}lkopf, Bernhard and Turner, Richard and Peters, Jonas},
eprint = {1507.05333},

month = {jul},
pages = {1--3},
title = {{Invariant Models for Causal Transfer Learning}},

year = {2015}
}
@article{Long2016,
abstract = {Domain adaptation generalizes a learning model across source domain and target domain that are sampled from different distributions. It is widely applied to cross-domain data mining for reusing labeled information and mitigating labeling consumption. Recent studies reveal that deep neural networks can learn abstract feature representation, which can reduce, but not remove, the cross-domain discrepancy. To enhance the invariance of deep representation and make it more transferable across domains, we propose a unified deep adaptation framework for jointly learning transferable representation and classifier to enable scalable domain adaptation, by taking the advantages of both deep learning and optimal two-sample matching. The framework constitutes two inter-dependent paradigms, unsupervised pre-training for effective training of deep models using deep denoising autoencoders, and supervised fine-tuning for effective exploitation of discriminative information using deep neural networks, both learned by embedding the deep representations to reproducing kernel Hilbert spaces (RKHSs) and optimally matching different domain distributions. To enable scalable learning, we develop a linear-time algorithm using unbiased estimate that scales linearly to large samples. Extensive empirical results show that the proposed framework significantly outperforms state of the art methods on diverse adaptation tasks: sentiment polarity prediction, email spam filtering, newsgroup content categorization, and visual object recognition.},
author = {Long, Mingsheng and Wang, Jianmin and Cao, Yue and Sun, Jiaguang and Yu, Philip S.},


isbn = {1041-4347 VO - 28},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Domain adaptation,deep learning,denoising autoencoder,multiple kernel learning,neural network,two-sample test},
number = {8},
pages = {2027--2040},
publisher = {IEEE},
title = {{Deep learning of transferable representation for scalable domain adaptation}},
volume = {28},
year = {2016}
}
@inproceedings{Zhang:2008:INL:1390156.1390311,
abstract = {Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and sampling, as the mainstream of such algorithms, has drawn considerable attention in both theory and practice. This paper presents detailed studies on the Nystr¨om sampling scheme and in particular, an error analysis that directly relates the Nystr¨om approximation quality with the encoding powers of the landmark points in summarizing the data. The resultant error bound suggests a simple and efficient sampling scheme, the k-means clustering algorithm, for Nystr¨om low-rank approximation. We compare it with state-of-the-art approaches that range from greedy schemes to probabilistic sampling. Our algorithm achieves significant performance gains in a number of supervised/ unsupervised learning tasks including kernel PCA and least squares SVM.},
address = {New York, NY, USA},
author = {Zhang, Kai and Tsang, Ivor W. and Kwok, James T.},
booktitle = {Proceedings of the 25th International Conference on Machine Learning},


isbn = {978-1-60558-205-4},
pages = {1232--1239},
publisher = {ACM},
series = {ICML '08},
title = {{Improved Nystr{\"{o}}m low-rank approximation and error analysis}},

year = {2008}
}
@inproceedings{6305619,
abstract = {Recently, data streams classification with concept drifting has drawn increasing attention of scholars in data mining, due to the deficiencies of existing algorithms in accuracy and efficient. In this paper, we propose a framework for handling the problem mentioned above using cluster decision tree. We cluster those data which cannot be classified temporarily into n class, and generate new branches of the VFDT based on cluster result or replace original ones. Our empirical study shows that the proposed method has substantial advantages over traditional classifiers in prediction accuracy and efficiency},
author = {Qian, Lin and Qin, Liang Xi},
booktitle = {Proceedings of the 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012},


isbn = {9780769547213},
keywords = {Classification,Cluster,Concept drifting,Data stream},
month = {aug},
pages = {38--41},
title = {{A framework of cluster decision tree in data stream classification}},
volume = {1},
year = {2012}
}
@inproceedings{Hsu_2019_CVPR_Workshops,
author = {Hsu, Han-Kai and Hung, Wei-Chih and Tseng, Hung-Yu and Yao, Chun-Han and Tsai, Yi-Hsuan and Singh, Maneesh and Yang, Ming-Hsuan},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

title = {{Progressive Domain Adaptation for Object Detection}},
year = {2019}
}
@article{Zhao2014,
abstract = {In this paper, we propose a novel machine learning framework called "Online Transfer Learning" (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain. We do not assume data in the target domain follows the same distribution as that in the source domain, and the motivation of our work is to enhance a supervised online learning task on a target domain by exploiting the existing knowledge that had been learnt from training data in source domains. OTL is in general very challenging since data in both source and target domains not only can be different in their class distributions, but also can be diverse in their feature representations. As a first attempt to this new research problem, we investigate two different settings of OTL: (i) OTL on homogeneous domains of common feature space, and (ii) OTL across heterogeneous domains of different feature spaces. For each setting, we propose effective OTL algorithms to solve online classification tasks, and show some theoretical bounds of the algorithms. In addition, we also apply the OTL technique to attack the challenging online learning tasks with concept-drifting data streams. Finally, we conduct extensive empirical studies on a comprehensive testbed, in which encouraging results validate the efficacy of our techniques. {\textcopyright} 2014 Elsevier B.V.},
author = {Zhao, Peilin and Hoi, Steven C.H. and Wang, Jialei and Li, Bin},


isbn = {0004-3702},
issn = {00043702},
journal = {Artificial Intelligence},
keywords = {Knowledge transfer,Online learning,Transfer learning},
pages = {76--102},
title = {{Online transfer learning}},
volume = {216},
year = {2014}
}
@inproceedings{Woo.2007,
abstract = {Data uncertainty is a common problem for the real-time monitoring of data streams. In this paper, we address the issue of efficiently monitoring the satisfaction/violation of user-defined constraints over data streams where the data uncertainty can be probabilistically characterized. We propose a monitoring architecture SPMON that can incorporate probabilistic models of uncertainty in constraint monitoring. We adapt the concept of data similarity in real-time databases to the processing of uncertain data streams. In doing so, we generalize the data similarity by a new concept psr (probabilistic similarity region) that allows us to define similarity relations for probabilistic data with respect to the set of constraints being monitored. This enables the construction of lightweight filters for saving bandwidth. We also show how to efficiently update the filter conditions at run-time.},
author = {Woo, Honguk and Mok, Aloysius K.},
booktitle = {Proceedings - Real-Time Systems Symposium},


isbn = {0769530621},
issn = {10528725},
pages = {288--297},
title = {{Real-time monitoring of uncertain data streams using probabilistic similarity}},

year = {2007}
}
@inproceedings{Kawashima.2008,
abstract = {This paper proposes the integration of probabilistic data streams and relational database by using Bayesian networks that is one of the most famous techniques for expressing uncertain contexts. A Bayesian network is expressed by the graphical model while relational data are expressed by relation. To integrate them we make the relational model as the unified model for its simplicity. A Bayesian network is modeled as an abstract data type in an object relational database, and we define signatures to extract a probabilistic relation from a Bayesian network. We provide a scheme to integrate a probabilistic relation and normal relations. To allow continual queries over streams for a Bayesian network, we introduce a new concept, lifespan.},
author = {Kawashima, Hideyuki and Sato, Ryo and Kitagawa, Hiroyuki},
booktitle = {Proceedings - 2008 International Symposium on Applications and the Internet, SAINT 2008},


isbn = {9780769532974},
pages = {157--160},
title = {{Models and issues on probabilistic data streams with Bayesian networks}},

year = {2008}
}
@article{takatsu2011,
author = {Takatsu, Asuka},

journal = {Osaka J. Math.},
number = {4},
pages = {1005--1026},
publisher = {Osaka University and Osaka City University, Departments of Mathematics},
title = {{Wasserstein geometry of Gaussian measures}},

volume = {48},
year = {2011}
}
@article{Xiao2019,
abstract = {Domain adaptation (DA) is one of the most promising techniques for leveraging existing knowledge from a source domain and applying it to a related target domain. Most DA methods mainly focus on learning a common subspace for the two domains by exploiting either the statistical property or the geometric structure independently to reduce the domain distribution difference. However, these two properties are complementary to each other, and jointly exploring them could yield optimal results. Inspired by the theoretical results of DA, in this paper, we propose structure preservation and distribution alignment (SPDA) in discriminative transfer subspace learning, which embeds the source domain classification error and reduction and domain distribution alignment into a single framework for optimization. SPDA learns an appropriate projection matrix, by which (1) the source domain classification error can be reduced; and (2) the source and target domain data are projected into a common subspace, where the domain distributions are well aligned and each target datum can be linearly reconstructed using the data from the source domain. To reduce the source domain classification error, an $\epsilon$-dragging technique that relaxes the strict binary label matrix is introduced to enlarge the distance between two data points from different classes. Further, the global subspace structure and the local geometric structure are preserved by imposing a low-rank constraint and a sparse constraint, respectively, on the reconstruction coefficient matrix. Moreover, the space relationship of the samples is preserved using a graph regularization method. In addition, marginal and conditional distributions between the domains are minimized to further reduce the domain shift statistically. We formulate source domain classifier design, geometric structure preservation, and distribution alignment as a rank-minimization problem, and we design an effective optimization algorithm based on the alternating direction method of multipliers (ADMM) to solve this problem. The functions and roles of each term in this framework are analyzed. The results of extensive experiments conducted on five datasets show that SPDA outperforms several state-of-the-art approaches and exhibits classification performance comparable with that of modern deep DA methods.},
author = {Xiao, Ting and Liu, Peng and Zhao, Wei and Liu, Hongwei and Tang, Xianglong},


issn = {09252312},
journal = {Neurocomputing},
keywords = {Distribution alignment,Domain adaptation,Low-rank representation,Structure preservation,Transfer learning},
month = {apr},
pages = {218--234},
publisher = {Elsevier B.V.},
title = {{Structure preservation and distribution alignment in discriminative transfer subspace learning}},

volume = {337},
year = {2019}
}
@article{Zisserman,
author = {{Aytar, Ysusuf, Zisserman}, Andrew},

journal = {International Conference on Computer Vision},
title = {{Tabula Rasa: Model Transfer for Object Category Detection}},
year = {2011}
}
@inproceedings{Mahadevan19,
abstract = {We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target domain can be reconciled using a unified mathematical framework that exploits the curved Riemannian geometry of statistical manifolds. We exploit a simple but important observation that as the space of covariance matrices is both a Riemannian space as well as a homogeneous space, the shortest path geodesic between two covariances on the manifold can be computed analytically. Statistics on the SPD matrix manifold, such as the geometric mean of two SPD matries can be reduced to solving the well-known Riccati equation. We show how the Ricatti-based solution can be constrained to not only reduce the statistical differences between the source and target domains, such as aligning second order covariances and minimizing the maximum mean discrepancy, but also the underlying geometry of the source and target domains using diffusions on the underlying source and target manifolds. Our solution also emerges as a consequence of optimal transport theory, which shows that the optimal transport mapping between source and target distributions that are multivariate Gaussians is a function of the geometric mean of the source and target covariances, a quantity that also minimizes the Wasserstein distance. A key strength of our proposed approach is that it enables integrating multiple sources of variation between source and target in a unified way, by reducing the combined objective function to a nested set of Ricatti equations where the solution can be represented by a cascaded series of geometric mean computations. In addition to showing the theoretical optimality of our solution, we present detailed experiments using standard transfer learning testbeds from computer vision comparing our proposed algorithms to past work in domain adaptation, showing improved results over a large variety of previous methods. Code related to this paper is available at: https://github.com/sridharmahadevan/Geodesic-Covariance-Alignment.},
address = {Cham},
archivePrefix = {arXiv},
arxivId = {arXiv:1804.10834v1},
author = {Mahadevan, Sridhar and Mishra, Bamdev and Ghosh, Shalini},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
editor = {Berlingerio, Michele and Bonchi, Francesco and G{\"{a}}rtner, Thomas and Hurley, Neil and Ifrim, Georgiana},
eprint = {arXiv:1804.10834v1},

isbn = {978-3-030-10928-8},
pages = {1--17},
publisher = {Springer International Publishing},
title = {{A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds}},
year = {2019}
}
@inproceedings{LongJAN2017,
author = {Long, Mingsheng and Zhu, Han and Wang, Jianmin and Jordan, Michael I},
booktitle = {Proceedings of the 34th International Conference on Machine Learning - Volume 70},

pages = {2208--2217},
publisher = {JMLR.org},
series = {ICML'17},
title = {{Deep Transfer Learning with Joint Adaptation Networks}},

year = {2017}
}
@book{8186985,
author = {Muthukrishnan, S},
booktitle = {Data Streams: Algorithms and Applications},
isbn = {9781933019147},
publisher = {now},
title = {{Data Streams: Algorithms and Applications}},

year = {2005}
}
@article{Zhang2013,
abstract = {Let X denote the feature and Y the target. We consider domain adaptation under three possible scenarios: (1) the marginal PYchanges, while the conditional PX|Ystays the same (target shift), (2) the marginal PYis fixed, while the conditional PX|Ychanges with certain constraints (conditional shift), and (3) the marginal PYchanges, and the conditional PX|Ychanges with constraints (generalized target shift). Using background knowledge, causal interpretations allow us to determine the correct situation for a problem at hand. We exploit importance reweighting or sample transformation to find the learning machine that works well on test data, and propose to estimate the weights or transformations by reweighting or transforming training data to reproduce the covariate distribution on the test domain. Thanks to kernel embedding of conditional as well as marginal distributions, the proposed approaches avoid distribution estimation, and are applicable for high-dimensional problems. Numerical evaluations on synthetic and real-world data sets demonstrate the effectiveness of the proposed framework. Copyright 2013 by the author(s).},
annote = {Parameter Search Procedure},
author = {Zhang, Kun and Schol̈kopf, Bernhard and Muandet, Krikamol and Wang, Zhikun},

journal = {30th International Conference on Machine Learning, ICML 2013},
number = {PART 3},
pages = {1856--1864},
title = {{Domain adaptation under target and conditional shift}},
volume = {28},
year = {2013}
}
@inproceedings{Zell1993,
author = {Zell, Andreas and H{\"{u}}bner, Ralf and Korb, Thomas and Mache, Niels and Mamier, G{\"{u}}nter and Schmalzl, Michael and Sommer, Tilman and Vogt, Michael},
booktitle = {MASCOTS '93: Proceedings of the International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems},
isbn = {1-56555-018-8},
pages = {343--346},
title = {{{\{}SNNS{\}}: An Efficient Simulator for Neural Nets}},
year = {1993}
}
@article{Blitzer2007,
abstract = {Empirical risk minimization offers well-known learning guarantees when training$\backslash$nand test data come from the same domain. In the real world, though, we often$\backslash$nwish to adapt a classifier from a source domain with a large amount of training$\backslash$ndata to different target domain with very little training data. In this work we$\backslash$ngive uniform convergence bounds for algorithms that minimize a convex$\backslash$ncombination of source and target empirical risk. The bounds explicitly model the$\backslash$ninherent trade-off between training on a large but inaccurate source data set$\backslash$nand a small but accurate target training set. Our theory also gives results when$\backslash$nwe have multiple source domains, each of which may have a different number of$\backslash$ninstances, and we exhibit cases in which minimizing a non-uniform combination of$\backslash$nsource risks can achieve much lower target error than standard empirical risk$\backslash$nminimization.},
author = {Blitzer, John and Crammer, Koby and Kulesza, Alex and Pereira, Fernando and Wortman, Jennifer},

isbn = {160560352X},
journal = {Neural Information Processing Systems (NIPS)},
number = {June},
pages = {129--136},
title = {{Learning Bounds for Domain Adaptation}},

volume = {20},
year = {2007}
}
@article{Oza2001,
abstract = {Bagging and boosting are well-known ensemble learning methods. They combine multiple learned base models with the aim of improving generalization performance. To date, they have been used primarily in batch mode, i.e., they require multiple passes through the training data. In previous work, we presented online bagging and boosting algorithms that only require one pass through the training data and presented experimental results on some relatively small datasets. Through additional experiments on a variety of larger synthetic and real datasets, this paper demonstrates that our online versions perform comparably to their batch counterparts in terms of classification accuracy. We also demonstrate the substantial reduction in running time we obtain with our online algorithms because they require fewer passes through the training data.},
author = {Oza, Nikunj C. and Russell, Stuart},


isbn = {158113391X},
issn = {158113391X},
journal = {Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining  - KDD '01},
pages = {359--364},
title = {{Experimental comparisons of online and batch versions of bagging and boosting}},

year = {2004}
}
@inproceedings{10.1007/978-3-319-91253-0_67,
abstract = {Classification learning by prototype based approaches is an attractive strategy to achieve interpretable classification models. Frequently, those models optimize the classification error or an approximation thereof. Current deep network approaches use the cross entropy maximization instead. Therefore, we propose a prototype based classifier based on cross-entropy as a probabilistic classifier. As we deduce, the proposed probabilistic classifier is a generalization of the robust soft-learning vector quantizer and allows to handle label noise in training data, i.e. the classifier is able to take into account probabilistic class assignments during learning.},
address = {Cham},
author = {Villmann, Andrea and Kaden, Marika and Saralajew, Sascha and Villmann, Thomas},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

editor = {Rutkowski, Leszek and {Scherer Rafa{\l}and Korytkowski}, Marcin and Pedrycz, Witold and Tadeusiewicz, Ryszard and Zurada, Jacek M},

isbn = {9783319912523},
issn = {16113349},
pages = {724--735},
publisher = {Springer International Publishing},
title = {{Probabilistic learning vector quantization with cross-entropy for probabilistic class assignments in classification learning}},
volume = {10841 LNAI},
year = {2018}
}
@article{Fernando2013,
author = {Fernando, Wen-sheng Chu and Torre, De and Cohn, Jeffery F},


journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,},
number = {3},
pages = {529--545},
title = {{Selective Transfer Machine for Personalized Facial Action Unit Detection}},
volume = {39},
year = {2016}
}
@inproceedings{Saralajew2016,
author = {Saralajew, Sascha and Villmann, Thomas},
booktitle = {2016 International Joint Conference on Neural Networks (IJCNN)},


isbn = {978-1-5090-0620-5},
month = {jul},
pages = {2672--2679},
publisher = {IEEE},
title = {{Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning}},

volume = {2016-Octob},
year = {2016}
}
@article{Demsar2006,
abstract = {Previous research has suggested that a mid-band of spatial frequencies is critical to face recognition in adults, but few studies have explored the development of this bias in children. We present a paradigm adapted from the adult literature to test spatial frequency biases throughout development. Faces were presented on a screen with particular spatial frequencies blocked out by noise masks. A mid-band bias was found in adults and 9- and 10-year-olds for upright faces but not for inverted faces, suggesting a face-sensitive effect. However, 7- and 8-year-olds did not demonstrate the mid-band bias for upright faces but rather processed upright and inverted faces similarly. This suggests that specialization toward the mid-band for upright face recognition develops gradually during childhood and may relate to an advanced level of face expertise. {\textcopyright} 2010 Elsevier Inc. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Leonard, Hayley C. and Karmiloff-Smith, Annette and Johnson, Mark H.},

eprint = {arXiv:1011.1669v3},

isbn = {9781424450404},
issn = {00220965},
journal = {Journal of Experimental Child Psychology},
keywords = {Configural,Development,Expertise,Face recognition,Featural,Spatial frequency},
number = {4},
pages = {193--207},
pmid = {20451214},
title = {{The development of spatial frequency biases in face recognition}},
volume = {106},
year = {2010}
}
@article{Weinberger2006,
abstract = {Can we detect low dimensional structure in high dimensional data sets of images? In this paper we propose an algorithm for unsupervised learning of image manifolds by semidefinite programming. Given a data set of images our algorithm computes a low dimensional representation of each image with the property that distances between nearby images are preserved. More generally it can be used to analyze high dimensional data that lies on or near a low dimensional manifold. We illustrate the algorithm on easily visualized examples of curves and surfaces as well as on actual images of faces handwritten digits and solid objects. {\textcopyright} 2006 Springer Science + Business Media, LLC.},
author = {Weinberger, Kilian Q. and Saul, Lawrence K.},


issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Data analysis,Dimensionality reduction,Image manifolds,Kernel methods,Manifold learning,Semidefinite embedding,Semidefinite programming},
month = {oct},
number = {1},
pages = {77--90},
title = {{Unsupervised Learning of Image Manifolds by Semidefinite Programming}},

volume = {70},
year = {2006}
}
@inproceedings{Hesse.2015,
abstract = {The present paper gives an overview about the state of the art technology within the area of data stream processing systems. Although the area of stream processing systems is not new, it is receiving a greater interest in the light of current business trends like the Internet of Things (IoT). The comparison of systems thereby includes several aspects such as a look into their architectures as well as into the responsibilities of the corresponding system components. A ranking or recommendations for one or more system(s) is not part of the work.},
author = {Hesse, Guenter and Lorenz, Martin},
booktitle = {Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS},


isbn = {9780769557854},
issn = {15219097},
keywords = {Flink,Samza,Spark,Storm,Stream Processing},
pages = {797--802},
title = {{Conceptual survey on data stream processing systems}},

volume = {2016-Janua},
year = {2016}
}
@inproceedings{Thopalli2019,
author = {Thopalli, Kowshik and Anirudh, Rushil and Thiagarajan, Jayaraman J. and Turaga, Pavan},
booktitle = {ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},


isbn = {978-1-4799-8131-1},
month = {may},
pages = {3552--3556},
publisher = {IEEE},
title = {{Multiple Subspace Alignment Improves Domain Adaptation}},

year = {2019}
}
@incollection{NIPS2012_4824,
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {Pereira, F and Burges, C J C and Bottou, L and Weinberger, K Q},

pages = {1097--1105},
publisher = {Curran Associates, Inc.},
title = {{ImageNet Classification with Deep Convolutional Neural Networks}},

year = {2012}
}
@article{Qian2018,
author = {Qian, Chen and Lu, Hengyang and Wang, Chongjun},


isbn = {9781538674499},
issn = {10823409},
journal = {Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI},
keywords = {Homogeneous transfer,Online learning,Online transfer learning,Transfer learning},
pages = {344--349},
title = {{Online single homogeneous source transfer learning based on adaboost}},
volume = {2018-Novem},
year = {2018}
}
@inproceedings{Singla2019,
author = {Singla, Ankush and Bertino, Elisa and Verma, Dinesh},
booktitle = {2019 IEEE International Conference on Smart Computing (SMARTCOMP)},


isbn = {978-1-7281-1689-1},
month = {jun},
number = {Ml},
pages = {69--74},
publisher = {IEEE},
title = {{Overcoming the Lack of Labeled Data: Training Intrusion Detection Models Using Transfer Learning}},

year = {2019}
}
@inproceedings{Shirai.2016,
abstract = {In this investigation, we propose transfer learning framework using latent intermediate domain for document stream classification. Transfer learning improves performance for target domain using the knowledge of source domain. Generally, it assumes related source and target, but not always such document set exists. We construct latent domain based on topic model from unlabeled documents. Our approach connect source domain and target domain by way of latent domain to classify document stream.},
author = {Shirai, Masato and Liu, Jianquan and Miura, Takao},
booktitle = {Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016},


isbn = {9781509021789},
keywords = {Document Classification,NMTF,Topic Model,Transfer Learning},
pages = {82--88},
title = {{Transfer learning using latent domain for document stream classification}},

year = {2016}
}
@article{Yuan2018,
abstract = {One of the hot topics in graph-based machine learning is to build Bayesian classifier from large-scale dataset. An advanced approach to Bayesian classification is based on exploited patterns. However, traditional pattern-based Bayesian classifiers cannot adapt to the evolving data stream environment. For that, an effective Pattern-based Bayesian classifier for Data Stream (PBDS) is proposed. First, a data-driven lazy learning strategy is employed to discover local frequent patterns for each test record. Furthermore, we propose a summary data structure for compact representation of data, and to find patterns more efficiently for each class. Greedy search and minimum description length combined with Bayesian network are applied to evaluating extracted patterns. Experimental studies on real-world and synthetic data streams show that PBDS outperforms most state-of-the-art data stream classifiers.},
author = {Yuan, Jidong and Wang, Zhihai and Sun, Yange and Zhang, Wei and Jiang, Jingjing},


issn = {18728286},
journal = {Neurocomputing},
keywords = {Bayesian,Data stream,Frequent pattern,Lazy learning},
pages = {17--28},
publisher = {Elsevier B.V.},
title = {{An effective pattern-based Bayesian classifier for evolving data stream}},

volume = {295},
year = {2018}
}
@article{Kumar.2012,
abstract = {The Nystr{\"{o}}m method is an efficient technique to generate low-rank matrix approximations and is used in several large-scale learning applications. A key aspect of this method is the procedure according to which columns are sampled from the original matrix. In this work, we explore the efficacy of a variety of fixed and adaptive sampling schemes. We also propose a family of ensemble based sampling algorithms for the Nystr{\"{o}}m method. We report results of extensive experiments that provide a detailed comparison of various fixed and adaptive sampling techniques, and demonstrate the performance improvement associated with the ensemble Nystr{\"{o}}m method when used in conjunction with either fixed or adaptive sampling schemes. Corroborating these empirical findings , we present a theoretical analysis of the Nystr{\"{o}}m method, providing novel error bounds guaranteeing a better convergence rate of the ensemble Nystr{\"{o}}m method in comparison to the standard Nystr{\"{o}}m method.},
author = {Talwalkar, Ameet and Kumar, Sanjiv and Mohri, Mehryar},

journal = {Journal of Machine Learning Research},
keywords = {ensemble methods,large-scale learning,low-rank approximation,nystr{\"{o}}m method},
pages = {981--1006},
title = {{Sampling Methods for the Nystr{\"{o}}m Method}},

volume = {13},
year = {2012}
}
@inproceedings{pmlr-v80-hoffman18a,
abstract = {Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models have shown tremendous progress towards adapting to new environments by focusing either on discovering domain invariant representations or by mapping between unpaired image domains. While feature space methods are difficult to interpret and sometimes fail to capture pixel-level and low-level domain shifts, image space methods sometimes fail to incorporate high level semantic knowledge relevant for the end task. We propose a model which adapts between domains using both generative image space alignment and latent representation space alignment. Our approach, Cycle-Consistent Adversarial Domain Adaptation (CyCADA), guides transfer between domains according to a specific discriminatively trained task and avoids divergence by enforcing consistency of the relevant semantics before and after adaptation. We evaluate our method on a variety of visual recognition and prediction settings, including digit classification and semantic segmentation of road scenes, advancing state-of-the-art performance for unsupervised adaptation from synthetic to real world driving domains.},
address = {Stockholmsm{\"{a}}ssan, Stockholm Sweden},
annote = {Authors extend Domain Adversarial Network to have following learnt properties:

1. Pixel Level alginment (Because high level adaptation does not consider pixel level adaptation)
2. Cycle Consitency: Map from source to target and back and vice versa with loss error.
3. High Level Alignment of representations.
Motiviation:
"The semantics losses both guide the overall representation to be discriminative and enforce semantic consistency before and after mapping between do- mains"


Parts of loss:
1. Cycle Consistente:
Learns Map GS from Source to Target and back to soruce minimizing the transformations error. The same with GT but from target to source to target. Implemented via L1 norm.

2. Semantic Consistency:
Difference of pediction of pretrained source network and predictiong of pretrained model given features from GS and GT are minimized. Given the current learning. Entropy.

3. GAN Consistency:
a) Minimize clf loss of domain classifer given GS features against target features
b) Minimize clf loss of domain clasisifer given GT feautres against target features
c) Standard DANN scenario on classifier level features from GS.

Of course also classification task of source data.},
author = {Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei and Darrell, Trevor},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
editor = {Dy, Jennifer and Krause, Andreas},

pages = {1989--1998},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {CyCADA: Cycle-Consistent Adversarial Domain Adaptation},

volume = {80},
year = {2018}
}
@article{Hou2017,
abstract = {Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g. action detection and recognition) has been limited due to complexity of video data and lack of annotations. Previous convolutional neural networks (CNN) based video action detection approaches usually consist of two major steps: frame-level action proposal detection and association of proposals across frames. Also, these methods employ two-stream CNN framework to handle spatial and temporal feature separately. In this paper, we propose an end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for action detection in videos. The proposed architecture is a unified network that is able to recognize and localize action based on 3D convolution features. A video is first divided into equal length clips and for each clip a set of tube proposals are generated next based on 3D Convolutional Network (ConvNet) features. Finally, the tube proposals of different clips are linked together employing network flow and spatio-temporal action detection is performed using these linked video proposals. Extensive experiments on several video datasets demonstrate the superior performance of T-CNN for classifying and localizing actions in both trimmed and untrimmed videos compared to state-of-the-arts.},
archivePrefix = {arXiv},
arxivId = {1703.10664},
author = {Hou, Rui and Chen, Chen and Shah, Mubarak},

eprint = {1703.10664},

isbn = {9781538610329},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {5823--5832},
title = {{Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos}},
volume = {2017-Octob},
year = {2017}
}
@article{Kang2018,
abstract = {In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN). First, our approach transfers knowledge in all the convolutional layers through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these methods, however, the convolutional layers which underpin critical low-level domain knowledge cannot be updated directly towards reducing domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based on this assumption, we propose an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain. Second, we estimate the posterior label distribution of the unlabeled data for target network training. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating the error accumulation of the estimated labels. The two contributions allow our approach to outperform the state-of-the-art methods by +2.6{\%} on the Office-31 dataset.},
author = {Kang, Guoliang and Zheng, Liang and Yan, Yan and Yang, Yi},


isbn = {9783030012519},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Attention,CycleGAN,Domain adaptation,EM},
pages = {420--436},
title = {{Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: The Benefit of Target Expectation Maximization}},
volume = {11215 LNCS},
year = {2018}
}
@inproceedings{Di2018,
abstract = {Transfer learning has gained increasing attention due to the infe- rior performance of machine learning algorithms with insufficient training data. Most of the previous homogeneous or heterogeneous transfer learning works aim to learn a mapping function between feature spaces based on the inherent correspondence across the source and target domains or labeled instances. However, in many real world applications, existing methods may not be robust when the correspondence across domains is noisy or labeled instances are not representative. In this paper, we develop a novel transfer learning framework called Transfer Learning via Feature Isomor- phism Discovery (abbreviated to TLFid), which owns high tolerance for noisy correspondence between domains as well as scarce or non-existing labeled instances. More specifically, we propose a fea- ture isomorphism approach to discovering common substructures across feature spaces and learning a feature mapping function from the target domain to the source domain.We evaluate the perfor- mance of TLFid on the cross-lingual sentiment classification tasks. The results showthat our method achieves significant improvement in terms of accuracy compared with the state-of-the-art methods.},
address = {New York, New York, USA},
author = {Di, Shimin and Peng, Jingshu and Shen, Yanyan and Chen, Lei},
booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery {\&} Data Mining - KDD '18},


isbn = {9781450355520},
keywords = {2018,Transfer learning,acm reference format,and lei chen,cross-lingual,jingshu peng,shimin di,subgraph isomorphism,transfer learn-,transfer learning,yanyan shen},
pages = {1301--1309},
publisher = {ACM Press},
title = {{Transfer Learning via Feature Isomorphism Discovery}},

year = {2018}
}
@article{Goldenberg2019,
abstract = {Deployed machine learning systems are necessarily learned from historical data and are often applied to current data. When the world changes, the learned models can lose fidelity. Such changes to the statistical properties of data over time are known as concept drift. Similarly, models are often learned in one context, but need to be applied in another. This is called concept shift. Quantifying the magnitude of drift or shift, especially in the context of covariate drift or shift, or unsupervised learning, requires use of measures of distance between distributions. In this paper, we survey such distance measures with respect to their suitability for estimating drift and shift magnitude between samples of numeric data.},
author = {Goldenberg, Igor and Webb, Geoffrey I.},


issn = {02193116},
journal = {Knowledge and Information Systems},
keywords = {Hellinger distance,Hotelling distance,Kullback–Leibler divergence,Mahalanobis distance,Multivariate concept drift},
number = {2},
pages = {591--615},
publisher = {Springer London},
title = {{Survey of distance measures for quantifying concept drift and shift in numeric data}},

volume = {60},
year = {2019}
}
@article{Barros2018,
abstract = {Online learning involves extracting information from large quantities of data (streams) usually affected by changes in the distribution (concept drift). A drift detector is a small program that estimates the positions of these changes to replace the base learner and ultimately improve overall accuracy. This article reports on a large-scale comparison of 14 concept drift detector configurations for mining fully labeled data streams with concept drift, using a large number of artificial datasets and two different base classifiers (Naive Bayes and Hoeffding Tree). The goal is to adequately measure how good the existent concept drift detectors really are and also to verify and challenge a common belief in the area, namely that the best drift detection methods are necessarily those that detect all the existing drifts closer to their correct positions, and only them, irrespective of the fact that different objectives usually require alternative solutions. Finally, to some extent, this article may also be seen as an extensive literature survey of concept drift detectors.},
author = {Barros, Roberto Souto Maior and Santos, Silas Garrido T.Carvalho},


issn = {00200255},
journal = {Information Sciences},
keywords = {Concept drift,Data stream,Drift detection,Large-scale comparison,Online learning},
pages = {348--370},
publisher = {Elsevier Inc.},
title = {{A large-scale comparison of concept drift detectors}},

volume = {451-452},
year = {2018}
}
@article{Montiel2019,
author = {Montiel, Jacob and Bifet, Albert and Losing, Viktor and Read, Jesse and Abdessalem, Talel},


isbn = {9781538650356},
journal = {Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018},
keywords = {Batch Learning,Classification,Concept Drift,Machine Learning,Stream Learning},
pages = {1065--1072},
publisher = {IEEE},
title = {{Learning Fast and Slow: A Unified Batch/Stream Framework}},
year = {2019}
}
@inproceedings{BMVC2015_24,
author = {Sun, Baochen and Saenko, Kate},
booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},

editor = {{Xianghua Xie Mark W. Jones} and Tam, Gary K L},

isbn = {1-901725-53-7},
month = {sep},
pages = {24.1--24.10},
publisher = {BMVA Press},
title = {{Subspace Distribution Alignment for Unsupervised Domain Adaptation}},

year = {2015}
}
@article{DBLP:journals/tkde/GuhaMMMO03,
abstract = {The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and clickstreams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. We describe such a streaming algorithm that effectively clusters large data streams. We also provide empirical evidence of the algorithm's performance on synthetic and real data streams.},
author = {Guha, Sudipto and Meyerson, Adam and Mishra, Nina and Motwani, Rajeev and O'Callaghan, Liadan},


issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Approximation algorithms,Clustering,Data streams},
number = {3},
pages = {515--528},
title = {{Clustering data streams: Theory and practice}},

volume = {15},
year = {2003}
}
@inproceedings{esann18_raab,
author = {Raab, Christoph and Heusinger, Moritz and Schleif, Frank Michael},
booktitle = {27th European Symposium on Artificial Neural Networks, {\{}ESANN{\}} 2019, Bruges, Belgium, April 24-26, 2019},

pages = {437--442},
title = {{Reactive Soft Prototype Computing for frequent reoccurring Concept Drift}},
year = {2019}
}
@inproceedings{Ge.2014,
abstract = {This paper proposes a novel naïve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a distance based and a density based algorithms to measure the correlations between feature vectors and class labels. We also devise a new pre-binning approach to guarantee bounded computation and memory cost in uncertain data streams classification. Experimental results in real uncertain data streams prove that our density-based naive classifier is efficient, accurate, and robust to data uncertainty.},
author = {Ge, Jiaqi and Xia, Yuni and Wang, Jian},
booktitle = {DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics},


isbn = {9781479969913},
pages = {392--398},
title = {{A na{\"{i}}ve Bayesian classifier in categorical uncertain data streams}},

year = {2014}
}
@article{DeMello2019a,
abstract = {Motivated by the Statistical Learning Theory (SLT), which provides a theoretical framework to ensure when supervised learning algorithms generalize input data, this manuscript relies on the Algorithmic Stability framework to prove learning bounds for the unsupervised concept drift detection on data streams. Based on such proof, we also designed the Plover algorithm to detect drifts using different measure functions, such as Statistical Moments and the Power Spectrum. In this way, the criterion for issuing data changes can also be adapted to better address the target task. From synthetic and real-world scenarios, we observed that each data stream may require a different measure function to identify concept drifts, according to the underlying characteristics of the corresponding application domain. In addition, we discussed about the differences of our approach against others from literature, and showed illustrative results confirming the usefulness of our proposal.},
author = {de Mello, Rodrigo F. and Vaz, Yule and Grossi, Carlos H. and Bifet, Albert},


issn = {09574174},
journal = {Expert Systems with Applications},
keywords = {Algorithmic stability,Concept drift,Data streams,McDiarmid's inequality},
month = {mar},
pages = {90--102},
publisher = {Elsevier Ltd},
title = {{On learning guarantees to unsupervised concept drift detection on data streams}},

volume = {117},
year = {2019}
}
@inproceedings{pmlr-v97-liu19b,
abstract = {Domain adaptation enables knowledge transfer from a labeled source domain to an unlabeled target domain. A mainstream approach is adversarial feature adaptation, which learns domain-invariant representations through aligning the feature distributions of both domains. However, a theoretical prerequisite of domain adaptation is the adaptability measured by the expected risk of an ideal joint hypothesis over the source and target domains. In this respect, adversarial feature adaptation may potentially deteriorate the adaptability, since it distorts the original feature distributions when suppressing domain-specific variations. To this end, we propose Transferable Adversarial Training (TAT) to enable the adaptation of deep classifiers. The approach generates transferable examples to fill in the gap between the source and target domains, and adversarially trains the deep classifiers to make consistent predictions over the transferable examples. Without learning domain-invariant representations at the expense of distorting the feature distributions, the adaptability in the theoretical learning bound is algorithmically guaranteed. A series of experiments validate that our approach advances the state of the arts on a variety of domain adaptation tasks in vision and NLP, including object recognition, learning from synthetic to real data, and sentiment classification.},
address = {Long Beach, California, USA},
author = {Liu, Hong and Long, Mingsheng and Wang, Jianmin and Jordan, Michael},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},

pages = {4013--4022},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers}},

volume = {97},
year = {2019}
}
@article{Wang2019d,
abstract = {Recent work in domain adaptation bridges different domains by adversarially learning a domain-invariant representation that cannot be distinguished by a domain discriminator. Existing methods of adversarial domain adaptation mainly align the global images across the source and target domains. However, it is obvious that not all regions of an image are transferable, while forcefully aligning the untransferable regions may lead to negative transfer. Furthermore, some of the images are significantly dissimilar across domains, resulting in weak image-level transferability. To this end, we present Transferable Attention for Domain Adaptation (TADA), focusing our adaptation model on transferable regions or images. We implement two types of complementary transferable attention: transferable local attention generated by multiple region-level domain discriminators to highlight transferable regions, and transferable global attention generated by single image-level domain discriminator to highlight transferable images. Extensive experiments validate that our proposed models exceed state of the art results on standard domain adaptation datasets.},
author = {Wang, Ximei and Li, Liang and Ye, Weirui and Long, Mingsheng and Wang, Jianmin},


issn = {2374-3468},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
month = {jul},
number = {01},
pages = {5345--5352},
publisher = {Association for the Advancement of Artificial Intelligence (AAAI)},
title = {{Transferable Attention for Domain Adaptation}},

volume = {33},
year = {2019}
}
@inproceedings{DBLP:conf/nips/BousmalisTSKE16,
author = {Bousmalis, Konstantinos and Trigeorgis, George and Silberman, Nathan and Krishnan, Dilip and Erhan, Dumitru},
booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain},

pages = {343--351},
title = {{Domain Separation Networks}},

year = {2016}
}
@article{Saengthongloun2013,
abstract = {Data stream classification is one of the most interesting problems in the data mining community. Recently, the idea of associative classification was introduced to handle data streams. However, single rule classification over data streams like AC-DS implicitly has two flaws. Firstly, it tends to produce a large bias on simple rules. Secondly, it is not appropriate for data streams that are slowly changed from time to time. To overcome this problem, we propose an algorithm, namely AC-Stream, for classifying a data stream using multiple rules. AC-Stream is able to find k-rules for predicting unseen data. An interval estimated Hoeffding-bound is used as a gain to approximate the best number of rules, k. Compared to AC-DS and other traditional associative classifiers on large number of UCI datasets, AC-Stream is more effective in terms of average accuracy and F1 measurement. {\textcopyright} 2013 IEEE.},
author = {Saengthongloun, Bordin and Kangkachit, Thanapat and Rakthanmanon, Thanawin and Waiyamai, Kitsana},


isbn = {9781479908066},
journal = {Proceedings of the 2013 10th International Joint Conference on Computer Science and Software Engineering, JCSSE 2013},
keywords = {associative - classification,data streams classification,multiple class-association rules},
pages = {223--228},
title = {{AC-Stream: Associative classification over data streams using multiple class association rules}},
year = {2013}
}
@article{Zeiler2012,
abstract = {We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters. We show promising results compared to other methods on the MNIST digit classification task using a single machine and on a large scale voice dataset in a distributed cluster environment.},
archivePrefix = {arXiv},
arxivId = {1212.5701},
author = {Zeiler, Matthew D.},
eprint = {1212.5701},

journal = {CoRR},
title = {{ADADELTA: An Adaptive Learning Rate Method}},

volume = {abs/1212.5},
year = {2012}
}
@article{Manoel2017,
abstract = {In statistical learning for real-world large-scale data problems, one must often resort to "streaming" algorithms which operate sequentially on small batches of data. In this work, we present an analysis of the information-theoretic limits of mini-batch inference in the context of generalized linear models and low-rank matrix factorization. In a controlled Bayes-optimal setting, we characterize the optimal performance and phase transitions as a function of mini-batch size. We base part of our results on a detailed analysis of a mini-batch version of the approximate message-passing algorithm (Mini-AMP), which we introduce. Additionally, we show that this theoretical optimality carries over into real-data problems by illustrating that Mini-AMP is competitive with standard streaming algorithms for clustering.},
archivePrefix = {arXiv},
arxivId = {1706.00705},
author = {Manoel, Andre and Krzakala, Florent and Tramel, Eric W. and Zdeborova, Lenka},

eprint = {1706.00705},

isbn = {9781538632666},
journal = {55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017},
number = {i},
pages = {1048--1055},
title = {{Streaming Bayesian inference: Theoretical limits and mini-batch approximate message-passing}},

volume = {2018-Janua},
year = {2018}
}
@article{Saito2018,
abstract = {In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics. To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at $\backslash$url{\{}https://github.com/mil-tokyo/MCD{\_}DA{\}}},
author = {Saito, Kuniaki and Watanabe, Kohei and Ushiku, Yoshitaka and Harada, Tatsuya},


isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {3723--3732},
title = {{Maximum Classifier Discrepancy for Unsupervised Domain Adaptation}},
year = {2018}
}
@article{WANG2018135,
abstract = {Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.},
author = {Wang, Mei and Deng, Weihong},


issn = {18728286},
journal = {Neurocomputing},
keywords = {Computer vision applications,Deep domain adaptation,Deep networks,Transfer learning},
pages = {135--153},
title = {{Deep visual domain adaptation: A survey}},

volume = {312},
year = {2018}
}
@article{Losing2017,
abstract = {Data Mining in non-stationary data streams is particularly relevant in the context of Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm. SAM-kNN can deal with heterogeneous concept drift, i.e different drift types and rates, using biologically inspired memory models and their coordination. Its basic idea are dedicated models for current and former concepts used according to the demands of the given situation. It can be robustly applied in practice without meta parameter optimization. We conduct an extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics and real world datasets. We explicitly add new benchmarks enabling a precise performance analysis on multiple types of drift. Highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift.},
author = {Losing, Viktor and Hammer, Barbara and Wersing, Heiko},


isbn = {9780999241103},
issn = {10450823},
journal = {IJCAI International Joint Conference on Artificial Intelligence},
keywords = {Artificial Intelligence: artificial intelligence,Artificial Intelligence: machine learning,Artificial Intelligence: other,Artificial Intelligence: uncertainty in artificial},
pages = {4899--4903},
title = {{Self-adjusting memory: How to deal with diverse drift types}},
year = {2017}
}
@article{Zhuang2019,
abstract = {Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. As the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Different from previous surveys, this survey paper reviews over forty representative transfer learning approaches from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.},
archivePrefix = {arXiv},
arxivId = {1911.02685},
author = {Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing},
eprint = {1911.02685},

journal = {CoR},
month = {nov},
number = {10},
pages = {1--27},
title = {{A Comprehensive Survey on Transfer Learning}},

year = {2019}
}
@article{Li2020,
author = {Li, Lusi and Wan, Zhiqiang and He, Haibo},


issn = {2168-2267},
journal = {IEEE Transactions on Cybernetics},
pages = {1--13},
title = {{Dual Alignment for Partial Domain Adaptation}},

year = {2020}
}
@inproceedings{Jin.2003,
abstract = {Decision tree construction is a well studied problem in data mining.  Recently, there has been much interest in mining streaming data.  Domingos and Hulten have presented a one-pass algorithm for decision tree construction.  Their work uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed.  In this paper, we revisit this problem.  We make the following two contributions: 1) We present a numerical interval pruning (NIP) approach for efficiently processing numerical attributes.  Our results show an average of 39{\%} reduction in execution times.  2) We exploit the properties of the gain function entropy (and gini) to reduce the sample size required for obtaining a given bound on the accuracy.  Our experimental results show a 37{\%} reduction in the number of data instances required.},
author = {Jin, Rouming and Agrawal, Gagan},
booktitle = {Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining  - KDD '03},


isbn = {1581137370},
pages = {571},
title = {{Efficient decision tree construction on streaming data}},

year = {2003}
}
@incollection{NIPS2019_8454,
author = {Cao, Jiezhang and Mo, Langyuan and Zhang, Yifan and Jia, Kui and Shen, Chunhua and Tan, Mingkui},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {1776--1786},
publisher = {Curran Associates, Inc.},
title = {{Multi-marginal Wasserstein GAN}},

year = {2019}
}
@article{THOMPSON19721,
abstract = {In this paper we stdy the singular values of the submatrices (not necessarily principal submatrices) of an arbitrary matrix A.},
author = {Thompson, R. C.},


issn = {00243795},
journal = {Linear Algebra and Its Applications},
number = {1},
pages = {1--12},
title = {{Principal submatrices IX: Interlacing inequalities for singular values of submatrices}},

volume = {5},
year = {1972}
}
@article{Huang2016a,
abstract = {Tens of thousands of chemicals with poorly understood biological properties are released into the environment each day. High-throughput screening (HTS) is potentially a more efficient and cost-effective alternative to traditional toxicity tests. Using HTS, one can profile chemicals for potential adverse effects and prioritize a manageable number for more in-depth testing. Importantly, it can provide clues to mechanism of toxicity. The Tox21 program has generated {\textgreater}50 million quantitative high-throughput screening (qHTS) data points. A library of several thousands of compounds, including environmental chemicals and drugs, is screened against a panel of nuclear receptor (NR) and stress response (SR) pathway assays. The National Center for Advancing Translational Sciences (NCATS) has organized an international data challenge in order to "crowd-source" data and build predictive toxicity models. This Challenge asks a "crowd" of researchers to use these data to elucidate the extent to which the interference of biochemical and cellular pathways by compounds can be inferred from chemical structure data. The data generated against the Tox21 library served as the training set for this modeling Challenge. The competition attracted participants from 18 different countries to develop computational models aimed at better predicting chemical toxicity. The winning models from nearly 400 model submissions all achieved {\textgreater} 80{\%} accuracy. Several models exceeded 90{\%} accuracy, which was measured by area under the receiver operating characteristic curve (AUC-ROC). Combining the winning models with the knowledge already gained from Tox21 screening data are expected to improve the community's ability to prioritize novel chemicals with respect to potential human health concern.},
author = {Huang, Ruili and Xia, Menghang and Nguyen, Dac-Trung and Zhao, Tongan and Sakamuru, Srilatha and Zhao, Jinghua and Shahane, Sampada A. and Rossoshek, Anna and Simeonov, Anton},


issn = {2296-665X},
journal = {Frontiers in Environmental Science},
keywords = {HTS,In vitro assay,Nuclear receptor,Predictive model,QSAR,Stress response,Tox21},
month = {jan},
number = {JAN},
pages = {1--9},
title = {{Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs}},

volume = {3},
year = {2016}
}
@article{Riedel2013,
abstract = {We propose in this contribution a method for l1-regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider the sparsity in the relevance profile enforced by LASSO optimization. The latter one is obtained by a gradient learning scheme using a differentiable parametrized approximation of the l1-norm, which has an upper error bound. We extend this regularization idea also to the matrix learning variant of LVQ as the natural generalization of relevance learning.},
author = {Riedel, M. and Rossi, F. and K{\"{a}}stner, M. and Villmann, T.},

isbn = {9782874190810},
journal = {ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
number = {April},
pages = {17--22},
title = {{Regularization in relevance learning vector quantization using l1-norms}},

year = {2013}
}
@article{Gower1975,
abstract = {SupposePi(i)(i = 1, 2, ...,m, j = 1, 2, ...,n) give the locations ofmn points inp-dimensional space. Collectively these may be regarded asm configurations, or scalings, each ofn points inp-dimensions. The problem is investigated of translating, rotating, reflecting and scaling them configurations to minimize the goodness-of-fit criterion {\$}\Sigma{\$}i=1m{\$}\Sigma{\$}i=1n{\$}\Delta{\$}2(Pj(i)Gi), whereGiis the centroid of them pointsPi(i)(i = 1, 2, ...,m). The rotated positions of each configuration may be regarded as individual analyses with the centroid configuration representing a consensus, and this relationship with individual scaling analysis is discussed. A computational technique is given, the results of which can be summarized in analysis of variance form. The special casem = 2 corresponds to Classical Procrustes analysis but the choice of criterion that fits each configuration to the common centroid configuration avoids difficulties that arise when one set is fitted to the other, regarded as fixed.},
author = {Gower, J C},


issn = {1860-0980},
journal = {Psychometrika},
number = {1},
pages = {33--51},
title = {{Generalized procrustes analysis}},

volume = {40},
year = {1975}
}
@incollection{NIPS2019_8546,
author = {Saralajew, Sascha and Holdijk, Lars and Rees, Maike and Asan, Ebubekir and Villmann, Thomas},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {2792--2803},
publisher = {Curran Associates, Inc.},
title = {{Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components}},

year = {2019}
}
@article{GrettonTwoSample2012,
author = {Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"{o}}lkopf, Bernhard and Smola, Alexander},

issn = {1532-4435},
journal = {J. Mach. Learn. Res.},
keywords = {hypothesis testing,integral probability metric,kernel methods,schema matching,two-sample test,uniform convergence bounds},
pages = {723--773},
publisher = {JMLR.org},
title = {{A Kernel Two-sample Test}},

volume = {13},
year = {2012}
}
@inproceedings{Tao.2009,
abstract = {Dynamic data streams are those whose underlying distribution changes over time. They occur in a number of application domains, and mining them is important for these applications. Coupled with the unboundedness and high arrival rates of data streams, the dynamism of the underlying distribution makes data mining challenging. In this paper, we focus on a large class of dynamic streams that exhibit periodicity in distribution changes. We propose a framework, called DMM, for mining this class of streams that includes a new change detection technique and a novel match-and-reuse approach. Once a distribution change is detected, we compare the new distribution with a set of historically observed distribution patterns and use the mining results from the past if a match is detected. Since, for two highly similar distributions, their mining results should also present high similarity, by matching and reusing existing mining results, the overall stream mining efficiency is improved while the accuracy is maintained. Our experimental results confirm this conjecture.},
author = {Tao, Yingying and Ozsu, M. Tamer},
booktitle = {Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2-6, 2009},


pages = {887},
title = {{Mining data streams with periodically changing distributions}},

year = {2009}
}
@article{Zhang2016,
abstract = {{\textcopyright} 2015 IEEE. Information Retrieval (IR) is concerned with indexing and retrieving documents including information relevant to a user's information need. Relevance Feedback (RF) is a class of effective algorithms for improving Information Retrieval (IR) and it consists of gathering further data representing the user's information need and automatically creating a new query. In this paper, we propose a class of RF algorithms inspired by quantum detection to re-weight the query terms and to re-rank the document retrieved by an IR system. These algorithms project the query vector on a subspace spanned by the eigenvector which maximizes the distance between the distribution of quantum probability of relevance and the distribution of quantum probability of non-relevance. The experiments showed that the RF algorithms inspired by quantum detection can outperform the state-of-the-art algorithms.},
archivePrefix = {arXiv},
arxivId = {1611.03530},
author = {Melucci, Massimo},

eprint = {1611.03530},

isbn = {1506.02142},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Information retrieval,quantum detection,quantum mechanics,relevance feedback},
number = {4},
pages = {1022--1034},
pmid = {88045},
title = {{Relevance Feedback Algorithms Inspired by Quantum Detection}},

volume = {28},
year = {2016}
}
@inproceedings{meda2018,
address = {New York, New York, USA},
annote = {Degenerated feature transformation means that both subspace
learning and distribution alignment can only reduce, but not re- move the distribution divergence [1]. Specifically,

Unevaluateddistribution alignment means that existingwork [19,
23, 33, 40] only attempted to align the marginal and conditional distributions with equal weights. But they failed to evaluate the relative importance of these two distributions.

Ration between marginal and conditional distribution with mu factor wich is the ratio between the error of seperating the domains. This contributes in the optimization problem in the MMD.

Problem:
MMD with class labels instead of features
p-MMD: Also with predicted classes but class dependend
Iterative reweigtening of Cond. and marginal distribution.
Laplace Regularization exploting similarities on the manifold

={\textgreater} FInal learning parameter: Learning Beta Paramter in the classification section

Learning Process:
Search for local minima in the derivateive with respect to beta

Experimental Results:
First: Task count, Mark how good the method is
Second: Descriebe worse behaviour of others
Third: Deep Methods
Foruth: Comparison between feature types

Efffectiveness Analysis:
Component wise evaluation
Behaviour with different paramters
Differenz between learned and optimal solution

Parameter Sensitivity:
Robustness of Parameters
Easy parameter search


Convergenve and Time
Convergence Criterion or guarantee
Table with features times sample size},
author = {Wang, Jindong and Feng, Wenjie and Chen, Yiqiang and Yu, Han and Huang, Meiyu and Yu, Philip S},
booktitle = {2018 ACM Multimedia Conference on Multimedia Conference - MM '18},


isbn = {9781450356657},
keywords = {distribution alignment,domain adaptation,subspace learning,transfer learning},
pages = {402--410},
publisher = {ACM Press},
series = {MM '18},
title = {{Visual Domain Adaptation with Manifold Embedded Distribution Alignment}},

year = {2018}
}
@inproceedings{Zantedeschi:2017:EDA:3128572.3140449,
abstract = {Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples.},
address = {New York, NY, USA},
author = {Zantedeschi, Valentina and Nicolae, Maria-Irina and Rawat, Ambrish},
booktitle = {Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security  - AISec '17},


isbn = {9781450352024},
keywords = {adversarial learning,deep neural network,defenses,model security},
pages = {39--49},
publisher = {ACM},
series = {AISec '17},
title = {{Efficient Defenses Against Adversarial Attacks}},

year = {2017}
}
@article{Brzezinski2014,
abstract = {Most stream classifiers are designed to process data incrementally, run in resource-aware environments, and react to concept drifts, i.e., unforeseen changes of the stream's underlying data distribution. Ensemble classifiers have become an established research line in this field, mainly due to their modularity which offers a natural way of adapting to changes. However, in environments where class labels are available after each example, ensembles which process instances in blocks do not react to sudden changes sufficiently quickly. On the other hand, ensembles which process streams incrementally, do not take advantage of periodical adaptation mechanisms known from block-based ensembles, which offer accurate reactions to gradual and incremental changes. In this paper, we analyze if and how the characteristics of block and incremental processing can be combined to produce new types of ensemble classifiers. We consider and experimentally evaluate three general strategies for transforming a block ensemble into an incremental learner: online component evaluation, the introduction of an incremental learner, and the use of a drift detector. Based on the results of this analysis, we put forward a new incremental ensemble classifier, called Online Accuracy Updated Ensemble, which weights component classifiers based on their error in constant time and memory. The proposed algorithm was experimentally compared with four state-of-the-art online ensembles and provided best average classification accuracy on real and synthetic datasets simulating different drift scenarios. {\textcopyright} 2013 Elsevier Inc. All rights reserved.},
author = {Brzezinski, Dariusz and Stefanowski, Jerzy},


issn = {00200255},
journal = {Information Sciences},
keywords = {Concept drift,Data stream,Ensemble,Online classifier},
pages = {50--67},
title = {{Combining block-based and online methods in learning ensembles from concept drifting data streams}},
volume = {265},
year = {2014}
}
@inproceedings{Kanagal.2008,
abstract = {In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.},
author = {Kanagal, Bhargav and Deshpande, Amol},
booktitle = {Proceedings - International Conference on Data Engineering},


isbn = {9781424418374},
issn = {10844627},
pages = {1160--1169},
title = {{Online filtering, smoothing and probabilistic modeling of streaming data}},

year = {2008}
}
@article{Morvant2011,
abstract = {Similarity functions are widely used in many machine learning or pattern recognition tasks. We consider here a recent framework for binary classification, proposed by Balcan et al., allowing to learn in a potentially non geometrical space based on good similarity functions. This framework is a generalization of the notion of kernels used in support vector machines in the sense that allows one to use similarity functions that do not need to be positive semi-definite nor symmetric. The similarities are then used to define an explicit projection space where a linear classifier with good generalization properties can be learned. In this paper, we propose to study experimentally the usefulness of similarity based projection spaces for transfer learning issues. More precisely, we consider the problem of domain adaptation where the distributions generating learning data and test data are somewhat different. We stand in the case where no information on the test labels is available. We show that a simple renormalization of a good similarity function taking into account the test data allows us to learn classifiers more performing on the target distribution for difficult adaptation problems. Moreover, this normalization always helps to improve the model when we try to regularize the similarity based projection space in order to move closer the two distributions. We provide experiments on a toy problem and on a real image annotation task. {\textcopyright} 2011 Springer-Verlag.},
author = {Morvant, Emilie and Habrard, Amaury and Ayache, St{\'{e}}phane},


isbn = {9783642244704},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Domain Adaptation,Good Similarity Functions,Image Classification,Transfer Learning},
pages = {1--16},
title = {{On the usefulness of similarity based projection spaces for transfer learning}},
volume = {7005 LNCS},
year = {2011}
}
@article{DosReis2016,
abstract = {Data stream research has grown rapidly over the last decade. Two major features distinguish data stream from batch learning: stream data are generated on the fly, possibly in a fast and variable rate; and the underlying data distribution can be non-stationary, leading to a phenomenon known as concept drift. Therefore, most of the research on data stream classification focuses on proposing efficient models that can adapt to concept drifts and maintain a stable performance over time. However, specifically for the classification task, the majority of such methods rely on the instantaneous availability of true labels for all already classified instances. This is a strong assumption that is rarely fulfilled in practical applications. Hence there is a clear need for efficient methods that can detect concept drifts in an unsupervised way. One possibility is the well-known Kolmogorov-Smirnov test, a statistical hypothesis test that checks whether two samples differ. This work has two main contributions. The first one is the Incremental Kolmogorov-Smirnov algorithm that allows performing the Kolmogorov-Smirnov hypothesis test instantly using two samples that change over time, where the change is an insertion and/or removal of an observation. Our algorithm employs a randomized tree and is able to perform the insertion and removal operations in O(log N) with high probability and calculate the Kolmogorov-Smirnov test in O(1), where N is the number of sample observations. This is a significant speed-up compared to the O(N log N) cost of the non-incremental implementation. The second contribution is the use of the Incremental Kolmogorov-Smirnov test to detect concept drifts without true labels. Classification algorithms adapted to use the test rely on a limited portion of those labels just to update the classification model after a concept drift is detected.},
author = {dos Reis, Denis Moreira and Flach, Peter and Matwin, Stan and Batista, Gustavo},


isbn = {9781450342322},
journal = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16},
keywords = {carte-,concept drift,data stream,kolmogorov-smirnov,lazy propagation,sian tree,treap},
number = {1},
pages = {1545--1554},
title = {{Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test}},

volume = {22},
year = {2016}
}
@inproceedings{Huang.2015,
abstract = {Massive data streams are continuously being generated from sources such as social media, broadcast news, etc., and typ-ically these datapoints lie in high-dimensional spaces (such as the vocabulary space of a language). Timely and accu-rate feature subset selection in these massive data streams has important applications in model interpretation, compu-tational/storage cost reduction, and generalization enhance-ment. In this paper, we introduce a novel unsupervised fea-ture selection approach on data streams that selects impor-tant features by making only one pass over the data while utilizing limited storage. The proposed algorithm uses ideas from matrix sketching to efficiently maintain a low-rank ap-proximation of the observed data and applies regularized regression on this approximation to identify the important features. We theoretically prove that our algorithm is close to an expensive offline approach based on global singular value decompositions. The experimental results on a vari-ety of text and image datasets demonstrate the excellent ability of our approach to identify important features even in presence of concept drifts and also its efficiency over other popular scalable feature selection algorithms.},
author = {Huang, Hao and Yoo, Shinjae and Kasiviswanathan, Shiva Prasad},
booktitle = {Proceedings of the 24th {\{}ACM{\}} International Conference on Information and Knowledge Management, {\{}CIKM{\}} 2015, Melbourne, VIC, Australia, October 19 - 23, 2015},


pages = {1031--1040},
title = {{Unsupervised Feature Selection on Data Streams}},

year = {2015}
}
@inproceedings{10.1007/978-3-642-16184-1_1,
abstract = {Micro-blogs are a challenging new source of information for data mining techniques. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. We briefly discuss the challenges that Twitter data streams pose, focusing on classification problems, and then consider these streams for opinion mining and sentiment analysis. To deal with streaming unbalanced classes, we propose a sliding window Kappa statistic for evaluation in time-changing data streams. Using this statistic we perform a study on Twitter data using learning algorithms for data streams.},
address = {Berlin, Heidelberg},
author = {Bifet, Albert and Frank, Eibe},
booktitle = {Discovery Science},
editor = {Pfahringer, Bernhard and Holmes, Geoff and Hoffmann, Achim},

isbn = {978-3-642-16184-1},
pages = {1--15},
publisher = {Springer Berlin Heidelberg},
title = {{Sentiment Knowledge Discovery in Twitter Streaming Data}},
year = {2010}
}
@article{Gopalan2011,
abstract = {Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.},
author = {Gopalan, Raghuraman and Li, Ruonan and Chellappa, Rama},


isbn = {9781457711015},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {999--1006},
publisher = {IEEE},
title = {{Domain adaptation for object recognition: An unsupervised approach}},
year = {2011}
}
@article{Liu2017a,
abstract = {Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning techniques have drawn ever-increasing research interests because of their inherent capability of overcoming the drawback of traditional algorithms dependent on hand-designed features. Deep learning approaches have also been found to be suitable for big data analysis with successful applications to computer vision, pattern recognition, speech recognition, natural language processing, and recommendation systems. In this paper, we discuss some widely-used deep learning architectures and their practical applications. An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different types of deep neural networks are surveyed and recent progresses are summarized. Applications of deep learning techniques on some selected areas (speech recognition, pattern recognition and computer vision) are highlighted. A list of future research topics are finally given with clear justifications.},
author = {Liu, Weibo and Wang, Zidong and Liu, Xiaohui and Zeng, Nianyin and Liu, Yurong and Alsaadi, Fuad E.},


issn = {09252312},
journal = {Neurocomputing},
keywords = {Autoencoder,Convolutional neural network,Deep belief network,Deep learning,Restricted Boltzmann machine},
month = {apr},
number = {November 2016},
pages = {11--26},
publisher = {Elsevier B.V.},
title = {{A survey of deep neural network architectures and their applications}},

volume = {234},
year = {2017}
}
@article{Bartges2017,
abstract = {Deep neural networks (DNN) have shown unprecedented success in various com-puter vision applications such as image classification and object detection. How-ever, it is still a common annoyance during the training phase, that one has to prepare at least thousands of labeled images to fine-tune a network to a specific domain. Recent study (Tommasi et al., 2015) shows that a DNN has strong de-pendency towards the training dataset, and the learned features cannot be easily transferred to a different but relevant task without fine-tuning. In this paper, we propose a simple yet powerful remedy, called Adaptive Batch Normalization (Ad-aBN) to increase the generalization ability of a DNN. By modulating the statistics in all Batch Normalization layers across the network, our approach achieves deep adaptation effect for domain adaptation tasks. In contrary to other deep learning domain adaptation methods, our method does not require additional components, and is parameter-free. It archives state-of-the-art performance despite its surpris-ing simplicity. Furthermore, we demonstrate that our method is complementary with other existing methods. Combining AdaBN with existing domain adaptation treatments may further improve model performance.},
archivePrefix = {arXiv},
arxivId = {1603.04779},
author = {Bartges, Joseph W},

eprint = {1603.04779},

journal = {Proteus},
pages = {441--446},
title = {{REVISITING BATCH NORMALIZATION FOR PRACTICAL DOMAIN ADAPTATION}},
year = {2017}
}
@inproceedings{Aljundi2015,
abstract = {Domain adaptation (DA) has gained a lot of success in the recent years in computer vision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, we introduce a novel unsupervised DA approach based on both subspace alignment and selection of landmarks similarly distributed between the two domains. Those landmarks are selected so as to reduce the discrepancy between the domains and then are used to non linearly project the data in the same space where an efficient subspace alignment (in closed-form) is performed. We carry out a large experimental comparison in visual domain adaptation showing that our new method outperforms the most recent unsupervised DA approaches.},
annote = {Closed Form of optimization problem.

Selection of points in target and source which are similar distributed in the respective domain.

Definition of overlap: Distance between the Gaussian Distribution of the similarity measure. Normalized

Project the data on subspace of gaussian kernel
={\textgreater}Kernel via selected points

PCA with eigenvectors performed by eigenvectors of target data.
Closed Form:
F(M) =k XSM−XT k2 F .
M = X′S* XT.},
author = {Aljundi, Rahaf and Emonet, Remi and Muselet, Damien and Sebban, Marc},
booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},


isbn = {978-1-4673-6964-0},
issn = {10636919},
month = {jun},
pages = {56--63},
publisher = {IEEE},
title = {{Landmarks-based kernelized subspace alignment for unsupervised domain adaptation}},

volume = {07-12-June},
year = {2015}
}
@article{Tsai2016,
abstract = {While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. This is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. The effectiveness of our proposed method can be successfully verified.},
author = {Tsai, Yao Hung Hubert and Yeh, Yi Ren and Wang, Yu Chiang Frank},


isbn = {9781467388504},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {5081--5090},
publisher = {IEEE},
title = {{Learning cross-domain landmarks for heterogeneous domain adaptation}},
volume = {2016-Decem},
year = {2016}
}
@article{Tian2020,
abstract = {Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain. The key issue of domain adaptation is how to reduce the distribution discrepancy between two domains in a proper way such that they can be treated indifferently for learning. Different from existing methods that make label prediction for target samples independently, in this paper, we propose a novel domain adaptation approach that assigns pseudo-labels to target data with the guidance of class centroids in two domains, so that the data distribution structure of both source and target domains can be emphasized. Besides, to explore the structure information of target data more thoroughly, we further introduce a local connectivity self-learning strategy into our proposal to adaptively capture the inherent local manifold structure of target samples. The aforementioned class centroid matching and local manifold self-learning are integrated into one joint optimization problem and an iterative optimization algorithm is designed to solve it with theoretical convergence guarantee. In addition to unsupervised domain adaptation, we further extend our method to the semi-supervised scenario including both homogeneous and heterogeneous settings in a direct but elegant way. Extensive experiments on five benchmark datasets validate the significant superiority of our proposal in both unsupervised and semi-supervised manners.},
archivePrefix = {arXiv},
arxivId = {2003.09391},
author = {Tian, Lei and Tang, Yongqiang and Hu, Liangchen and Ren, Zhida and Zhang, Wensheng},
eprint = {2003.09391},

journal = {arXiv},
month = {mar},
pages = {1--14},
title = {{Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning}},

year = {2020}
}
@article{Lu2015a,
abstract = {Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, computational intelligence has recently been applied in transfer learning. This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories: (a) neural network-based transfer learning; (b) Bayes-based transfer learning; (c) fuzzy transfer learning, and (d) applications of computational intelligence-based transfer learning. By providing state-of-the-art knowledge, this survey will directly support researchers and practice-based professionals to understand the developments in computational intelligence-based transfer learning research and applications.},
archivePrefix = {arXiv},
arxivId = {1511.05641},
author = {Lu, Jie and Behbood, Vahid and Hao, Peng and Zuo, Hua and Xue, Shan and Zhang, Guangquan},

eprint = {1511.05641},

isbn = {0950-7051},
issn = {09507051},
journal = {Knowledge-Based Systems},
keywords = {Bayes,Computational intelligence,Fuzzy sets and systems,Genetic algorithm,Neural network,Transfer learning},
pages = {14--23},
publisher = {Elsevier B.V.},
title = {{Transfer learning using computational intelligence: A survey}},

volume = {80},
year = {2015}
}
@inproceedings{Jing2019,
author = {Jing, Mengmeng and Li, Jingjing and Lu, Ke and Liu, Jieyan and Huang, Zi},
booktitle = {2019 IEEE International Conference on Multimedia and Expo (ICME)},


isbn = {978-1-5386-9552-4},
month = {jul},
number = {3},
pages = {1660--1665},
publisher = {IEEE},
title = {{Adaptive Component Embedding for Unsupervised Domain Adaptation}},

year = {2019}
}
@inproceedings{daume-iii-2007-frustratingly,
address = {Prague, Czech Republic},
author = {{Daum{\'{e}} III}, Hal},
booktitle = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics},

pages = {256--263},
publisher = {Association for Computational Linguistics},
title = {{Frustratingly Easy Domain Adaptation}},

year = {2007}
}
@inproceedings{Kirstein2005,
abstract = {We present an approach for the supervised online learning of object representations based on a biologically motivated architecture of visual processing. We use the output of a recently developed topographical feature hierarchy to provide a view-based representation of three-dimensional objects using a dynamical vector quantization approach. For a simple short-term object memory model we demonstrate real-time online learning of 50 complex-shaped objects within three hours. Additionally we propose some modifications of learning vector quantization algorithms that are especially adapted to the task of online learning and capable of effectively reducing the representational effort in a transfer from short-term to long-term memory.},
address = {Berlin, Heidelberg},
author = {Kirstein, Stephan and Wersing, Heiko and K{\"{o}}rner, Edgar},
booktitle = {Pattern Recognition},

editor = {Kropatsch, Walter G and Sablatnig, Robert and Hanbury, Allan},

isbn = {978-3-540-31942-9},
pages = {301--308},
publisher = {Springer Berlin Heidelberg},
title = {{Rapid Online Learning of Objects in a Biologically Motivated Recognition Architecture}},
year = {2005}
}
@article{Li2019,
abstract = {Domain Adaptation aims at utilizing source data to establish an exact model for a related but different target domain. In recent years, many effective models have been proposed to propagate label information across domains. However, these models rely on large-scale labeled data in source domain and cannot handle the case where the source domain lacks label information. In this paper, we put forward a Graph Regularized Domain Adaptation (GDA) to tackle this problem. Specifically, the proposed GDA integrates graph regularization with maximum mean discrepancy (MMD). Hence GDA enables sufficient unlabeled source data to facilitate knowledge transfer by utilizing the geometric property of source domain, simultaneously, due to the embedding of MMD, GDA can reduce source and target distribution divergency to learn a generalized classifier. Experimental results validate that our GDA outperforms the traditional algorithms when there are few labeled source samples.},
author = {Li, Jinfeng and Liu, Weifeng and Zhou, Yicong and Tao, Dapeng and Nie, Liqiang},


issn = {1370-4621},
journal = {Neural Processing Letters},
month = {jul},
title = {{Domain Adaptation with Few Labeled Source Samples by Graph Regularization}},

year = {2019}
}
@article{Brehmer2020,
abstract = {We introduce manifold-modeling flows (MFMFs), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold. Combining aspects of normalizing flows, GANs, autoencoders, and energy-based models, they have the potential to represent data sets with a manifold structure more faithfully and provide handles on dimensionality reduction, denoising, and out-of-distribution detection. We argue why such models should not be trained by maximum likelihood alone and present a new training algorithm that separates manifold and density updates. With two pedagogical examples we demonstrate how manifold-modeling flows let us learn the data manifold and allow for better inference than standard flows in the ambient data space.},
archivePrefix = {arXiv},
arxivId = {2003.13913},
author = {Brehmer, Johann and Cranmer, Kyle},
eprint = {2003.13913},

journal = {arXiv},
month = {mar},
title = {{Flows for simultaneous manifold learning and density estimation}},

year = {2020}
}
@article{Sadiq2018,
abstract = {The main assumption in machine learning and data mining is, training the data, and the future data have the same distribution and same features. However, in many applications, in the real world, such assumptions may not be retained. For example, sometimes, we have the task of classification in the one domain of interest, but when the same data is used in another domain, it needed enough training to work in the other domain of interest. In the field of heterogeneous transfer learning, train the data in one domain and test with other domain. In this case, knowledge is transfer; if there is a successful transfer, it can significantly improve performance by avoiding the learning in the label information which is more expensive. Over the past few years, the transfer learning has become a new learning framework to address this issue and heterogeneous transfer learning is the most active research area in the recent years. In this study, we are discussing the relationship between heterogeneous transfer learning and the other machine learning methods, including the field of adaptation, learning and multitasking learning and sample selection bias, as well as the associates of variables. We also reconnoiter some main challenges for the future issue in heterogeneous transfer learning.},
author = {Iqbal, Muhammad Shahid and Luo, Bin and Khan, Tamoor and Mehmood, Rashid and Sadiq, Muhammad},


issn = {2520-8438},
journal = {Iran Journal of Computer Science},
keywords = {Transfer learning,Heterogeneous,Symmetric transfer,asymmetric transfer,heterogeneous,symmetric transfer,transfer learning},
number = {1},
pages = {31--46},
publisher = {Springer International Publishing},
title = {{Heterogeneous transfer learning techniques for machine learning}},

volume = {1},
year = {2018}
}
@article{Yu2019,
abstract = {A fundamental issue for statistical classification models in a streaming environment is that the joint distribution between predictor and response variables changes over time (a phenomenon also known as concept drifts), such that their classification performance deteriorates dramatically. In this paper, we first present a hierarchical hypothesis testing (HHT) framework that can detect and also adapt to various concept drift types (e.g., recurrent or irregular, gradual or abrupt), even in the presence of imbalanced data labels. A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR), is implemented under the HHT framework thereafter. By substituting a widely-acknowledged retraining scheme with an adaptive training strategy, we further demonstrate that the concept drift adaptation capability of HLFR can be significantly boosted. The theoretical analysis on the Type-I and Type-II errors of HLFR is also performed. Experiments on both simulated and real-world datasets illustrate that our methods outperform state-of-the-art methods in terms of detection precision, detection delay as well as the adaptability across different concept drift types.},
author = {Yu, Shujian and Abraham, Zubin and Wang, Heng and Shah, Mohak and Wei, Yantao and Pr{\'{i}}ncipe, Jos{\'{e}} C.},


issn = {00160032},
journal = {Journal of the Franklin Institute},
month = {mar},
number = {5},
pages = {3187--3215},
publisher = {Elsevier Ltd},
title = {{Concept drift detection and adaptation with hierarchical hypothesis testing}},

volume = {356},
year = {2019}
}
@article{Zhong2010,
abstract = {One solution to the lack of label problem is to exploit transfer learning, whereby one acquires knowledge from source-domains to improve the learning performance in the target-domain. The main challenge is that the source and target domains may have different distributions. An open problem is how to select the available models (including algorithms and parameters) and importantly, abundance of source-domain data, through statistically reliable methods, thus making transfer learning practical and easy-to-use for real-world applications. To address this challenge, one needs to take into account the difference in both marginal and conditional distributions in the same time, but not just one of them. In this paper, we formulate a new criterion to overcome "double" distribution shift and present a practical approach "Transfer Cross Validation" (TrCV) to select both models and data in a cross validation framework, optimized for transfer learning. The idea is to use density ratio weighting to overcome the difference in marginal distributions and propose a "reverse validation" procedure to quantify how well a model approximates the true conditional distribution of target-domain. The usefulness of TrCV is demonstrated on different cross-domain tasks, including wine quality evaluation, web-user ranking and text categorization. The experiment results show that the proposed method outperforms both traditional cross-validation and one state-of-the-art method which only considers marginal distribution shift. The software and datasets are available from the authors. {\textcopyright} 2010 Springer-Verlag Berlin Heidelberg.},
author = {Zhong, Erheng and Fan, Wei and Yang, Qiang and Verscheure, Olivier and Ren, Jiangtao},


isbn = {3642159389},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
number = {PART 3},
pages = {547--562},
title = {{Cross validation framework to choose amongst models and datasets for transfer learning}},
volume = {6323 LNAI},
year = {2010}
}
@inproceedings{Shetty.2011,
abstract = {Anomaly-based network Intrusion Detection Systems (IDS) model patterns of normal activity and detect novel network attacks. However, these systems depend on the availability of the systems normal traffic pattern profile. But the statistical fingerprint of the normal traffic pattern can change and shift over a period of time due to changes in operational or user activity at the networked site or even system updates. The changes in normal traffic patterns over time lead to concept drift. Some changes can be temporal, cyclical and can be short-lived or they can last for longer periods of time. Depending on a number of factors the speed at which the change in traffic patterns occurs can also be variable, ranging from near instantaneous to the change occurring over the span of numerous months. These changes in traffic patterns are a cause of concern for IDSs as they can lead to a significant increase in false positive rates, thereby reducing the overall system performance. In order to improve the reliability of the IDS, there is a need for an automated mechanism to detect valid traffic changes and avoid inappropriate ad hoc responses. ROC curves have historically been used to evaluate the accuracy of IDSs. ROC curves generated using fixed, time-invariant classification thresholds do not characterize the best accuracy that an IDS can achieve in presence of concept-drifting network traffic. In this paper, we present a integrated supervised machine learning and control theoretic model for detecting concept drift in network traffic patterns. The model comprises of a online support vector machine based classifier(incremental anomaly based detection), a Kullback - Leibler divergence based relative entropy measurement scheme(quantifying concept drift) and feedback control engine(adapting ROC thresholding). In our proposed system, any intrusion activity will cause significant variations, thereby causing a large error, while a minor aberration in the variations (concept drift) w- ll not be immediately reported as alert.},
author = {Shetty, Sachin and Mukkavilli, Sai Kiran and Keel, L. H.},
booktitle = {2011 IEEE International Conference on Technologies for Homeland Security, HST 2011},


isbn = {9781457713750},
keywords = {Anomaly Based Intrusion Detection Systems,Concept Drift,Support Vector Machine},
month = {nov},
pages = {75--80},
title = {{An integrated machine learning and control theoretic model for mining concept-drifting data streams}},
year = {2011}
}
@inproceedings{Yu.2017,
abstract = {This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i.i.d. generated at each round and are disclosed to the decision maker only after the decision is made. This formulation arises naturally when decisions are restricted by stochastic environments or deterministic environments with noisy observations. It also includes many important problems as special cases, such as OCO with long term constraints, stochastic constrained convex optimization, and deterministic constrained convex optimization. To solve this problem, this paper proposes a new algorithm that achieves {\$}O(\backslashsqrt{\{}T{\}}){\$} expected regret and constraint violations and {\$}O(\backslashsqrt{\{}T{\}}\backslashlog(T)){\$} high probability regret and constraint violations. Experiments on a real-world data center scheduling problem further verify the performance of the new algorithm.},
archivePrefix = {arXiv},
arxivId = {1708.03741},
author = {Yu, Hao and Neely, Michael J. and Wei, Xiaohan},
booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, {\{}USA{\}}},
eprint = {1708.03741},

pages = {1427--1437},
title = {{Online Convex Optimization with Stochastic Constraints}},

year = {2017}
}
@article{Lughofer2008a,
abstract = {In this paper, we describe a new clustering-based classification technique (eVQ-Class), which is able to adapt old clusters and to evolve new ones on-line with new incoming data samples. It extends the conventional learning vector quantization approach, which is a kind of supervised version of original vector quantization, in mainly three points: 1.) it is able toevolve new clusters on demand by comparing new incoming samples with already generated clusters, 2.) it includes the label information in the training process by introducing a hit matrix and extending the feature space and 3.) it comes with a new weighted classification strategy.The novel approach will be evaluated based on high-dimensional feature data sets extracted from images recorded on-line in order to perform on-line quality control in a production process by classifying images into 'good' and 'bad' ones. The evaluation includes a comparison with well-known batch (trained and re-trained) classification techniques.},
author = {Lughofer, Edwin},


isbn = {9780769535142},
journal = {2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008},
keywords = {learning vector quantization, cluster evolution, w},
pages = {779--784},
title = {{Evolving vector quantization for classification of on-line data streams}},
year = {2008}
}
@article{RaabNBTArx,
abstract = {Transfer learning focuses on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these areas, learning scenarios change by nature, but often remain related and motivate the reuse of existing supervised models. While the majority of symmetric and asymmetric domain adaptation algorithms utilize all available source and target domain data, we show that domain adaptation requires only a substantial smaller subset. This makes it more suitable for real-world scenarios where target domain data is rare. The presented approach finds a target subspace representation for source and target data to address domain differences by orthogonal basis transfer. We employ Nystr$\backslash$"om techniques and show the reliability of this approximation without a particular landmark matrix by applying post-transfer normalization. It is evaluated on typical domain adaptation tasks with standard benchmark data.},
archivePrefix = {arXiv},
arxivId = {1907.01343},
author = {Raab, Christoph and Schleif, Frank-Michael},
eprint = {1907.01343},
journal = {CoRR},
month = {jul},
title = {{Domain Adaptation via Low-Rank Basis Approximation}},

volume = {abs/1907.0},
year = {2019}
}
@inproceedings{Sutton1986,
abstract = {This article contributes to the theory of network learning procedures by identifying and analyzing two problems with the backpropagation procedure of Rumelhart, Hinton, and Williams (1985) that may slow its learning. Both problems are due to backpropaga- tion's being a gradient- or steepest-descent method in the weight space of the network. The first problem is that steepest descent is a particularly poor descent procedure for surfaces containing ravines-places which curve more sharply in some directions than others-and such ravines are common and pronounced in performance surfaces arising from networks. The second problem is that steepest descent results in a high level of interference between learning with different patterns, because those units that have so far been found most use- ful are also those most likely to be changed to handle new patterns. The same problems probably also arise with the Boltzmann machine learning procedure (Ackley, Hinton and Sejnowski, 1985) and with reinforcement learning procedures (Barto and Anderson, 1985), as these are also steepest-descent procedures. Finally, some directions in which to look for improvements to backpropagation based on alternative descent procedures are briefly considered.},
author = {Sutton, Richard S},
booktitle = {Proc. 8th annual conf. cognitive science society},
keywords = {nn},
pages = {823--831},
publisher = {Hillsdale, NJ: Erlbaum},
title = {{Two problems with backpropagation and other steepest-descent learning procedures for networks}},
year = {1986}
}
@article{Liu2009,
abstract = {For mining new pattern from evolving data streams, most algorithms are inherited from DenStream framework which is realized via a sliding window. So at the early stage of a pattern emerges, its knowledge points can be easily mistaken as outliers and dropped. In most cases, these points can be ignored, but in some special applications which need to quickly and precisely master the emergence rule of some patterns, these points must play their rules. Based on DenStream, this paper proposes a three-step clustering algorithm, rDenStream, which presents the concept of outlier retrospect. In rDenStream clustering, dropped micro-clusters are stored on outside memory temporarily, and will be given new chance to attend clustering to improve the clustering accuracy. Experiments modeled the arrival of data stream in Poisson process, and the results over standard data set showed its advantage over other methods in the early phase of new pattern discovery.},
author = {Liu, Li Xiong and Guo, Yun Fei and Huang, Hai and Chen, Fu Cai},


isbn = {9781424449941},
issn = {2156-7379},
journal = {Proceedings - 2009 International Conference on Information Engineering and Computer Science, ICIECS 2009},
keywords = {Clustering,Data mining,Evolving data streams,Poisson process,Retrospect learning},
pages = {1--4},
title = {{rDenStream,A clustering algorithm over an evolving data stream}},

year = {2009}
}
@inproceedings{Raghunathan.2017,
abstract = {In this paper, we study the problem of learning a mixture of Gaussians with streaming data: given a stream of {\$}N{\$} points in {\$}d{\$} dimensions generated by an unknown mixture of {\$}k{\$} spherical Gaussians, the goal is to estimate the model parameters using a single pass over the data stream. We analyze a streaming version of the popular Lloyd's heuristic and show that the algorithm estimates all the unknown centers of the component Gaussians accurately if they are sufficiently separated. Assuming each pair of centers are {\$}C\backslashsigma{\$} distant with {\$}C=\backslashOmega((k\backslashlog k){\^{}}{\{}1/4{\}}\backslashsigma){\$} and where {\$}\backslashsigma{\^{}}2{\$} is the maximum variance of any Gaussian component, we show that asymptotically the algorithm estimates the centers optimally (up to constants); our center separation requirement matches the best known result for spherical Gaussians $\backslash$citep{\{}vempalawang{\}}. For finite samples, we show that a bias term based on the initial estimate decreases at {\$}O(1/{\{}\backslashrm poly{\}}(N)){\$} rate while variance decreases at nearly optimal rate of {\$}\backslashsigma{\^{}}2 d/N{\$}. Our analysis requires seeding the algorithm with a good initial estimate of the true cluster centers for which we provide an online PCA based clustering algorithm. Indeed, the asymptotic per-step time complexity of our algorithm is the optimal {\$}d\backslashcdot k{\$} while space complexity of our algorithm is {\$}O(dk\backslashlog k){\$}. In addition to the bias and variance terms which tend to {\$}0{\$}, the hard-thresholding based updates of streaming Lloyd's algorithm is agnostic to the data distribution and hence incurs an approximation error that cannot be avoided. However, by using a streaming version of the classical (soft-thresholding-based) EM method that exploits the Gaussian distribution explicitly, we show that for a mixture of two Gaussians the true means can be estimated consistently, with estimation error decreasing at nearly optimal rate, and tending to {\$}0{\$} for {\$}N\backslashrightarrow \backslashinfty{\$}.},
archivePrefix = {arXiv},
arxivId = {1707.02391},
author = {Raghunathan, Aditi and Krishnaswamy, Ravishankar and Jain, Prateek},
booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, {\{}USA{\}}},
eprint = {1707.02391},

pages = {6608--6617},
title = {{Learning Mixture of Gaussians with Streaming Data}},

year = {2017}
}
@incollection{Climer2016a,
address = {Cham},
author = {Climer, Jonathon and Mendenhall, Michael J.},
booktitle = {Advances in Intelligent Systems and Computing},

editor = {Mer{\'{e}}nyi, Erzs{\'{e}}bet and Mendenhall, Michael J. and O'Driscoll, Patrick},

isbn = {978-3-319-28517-7},
issn = {21945357},
keywords = {classifier,cost minimization,dynamic,incremental learning vector quantization},
pages = {355--368},
publisher = {Springer International Publishing},
series = {Advances in Intelligent Systems and Computing},
title = {{Dynamic Prototype Addition in Generalized Learning Vector Quantization}},

volume = {428},
year = {2016}
}
@article{Chen2014a,
abstract = {The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework. {\textcopyright} 2012 IEEE.},
archivePrefix = {arXiv},
arxivId = {1210.8291},
author = {Chen, Huanhuan and Tino, Peter and Rodan, Ali and Yao, Xin},

eprint = {1210.8291},

issn = {2162237X},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
keywords = {Cognitive fault diagnosis,fault detection,learning in the model space,one class learning,reservoir computing (RC)},
number = {1},
pages = {124--136},
title = {{Learning in the model space for cognitive fault diagnosis}},
volume = {25},
year = {2014}
}
@article{Yang2018,
abstract = {Unsupervised domain adaptation has been proved to be a promising approach to solve the problem of dataset bias. To employ source labels in the target domain, it is required to align the joint distributions of source and target data. To do this, the key research problem is to align conditional distributions across domains without target labels. In this paper , we propose a new criterion of domain-shared group-sparsity that is an equivalent condition for conditional distribution alignment. To solve the problem in joint distribution alignment, a domain-shared group-sparse dictionary learning method is developed towards joint alignment of conditional and marginal distributions. A classifier for target domain is trained using the domain-shared group-sparse coefficients and the target-specific information from the target data. Experimental results on cross-domain face and object recognition show that the proposed method outperforms eight state-of-the-art unsupervised domain adaptation algorithms.},
author = {Yang, Baoyao and Ma, Andy J and Yuen, Pong C},

journal = {Aaai},
keywords = {VIsion Track},
pages = {7453--7460},
title = {{Domain-Shared Group-Sparse Dictionary Learning for Unsupervised Domain Adaptation}},
year = {2018}
}
@incollection{NIPS2016_6136,
author = {Kumagai, Wataru},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {Lee, D D and Sugiyama, M and Luxburg, U V and Guyon, I and Garnett, R},

pages = {2721--2729},
publisher = {Curran Associates, Inc.},
title = {{Learning Bound for Parameter Transfer Learning}},

year = {2016}
}
@article{DBLP:journals/corr/abs-1102-5597,
abstract = {With the explosion of the size of digital dataset, the limiting factor for decomposition algorithms is the $\backslash$emph{\{}number of passes{\}} over the input, as the input is often stored out-of-core or even off-site. Moreover, we're only interested in algorithms that operate in $\backslash$emph{\{}constant memory{\}} w.r.t. to the input size, so that arbitrarily large input can be processed. In this paper, we present a practical comparison of two such algorithms: a distributed method that operates in a single pass over the input vs. a streamed two-pass stochastic algorithm. The experiments track the effect of distributed computing, oversampling and memory trade-offs on the accuracy and performance of the two algorithms. To ensure meaningful results, we choose the input to be a real dataset, namely the whole of the English Wikipedia, in the application settings of Latent Semantic Analysis.},
archivePrefix = {arXiv},
arxivId = {1102.5597},
author = {Řeh{\{}ů{\}}řek, Radim},
eprint = {1102.5597},

journal = {CoRR},
title = {{Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms}},

volume = {abs/1102.5},
year = {2011}
}
@inproceedings{Elhadji-Ille-Gado2018,
annote = {Same as SA but with FESVD (efficient low rank approximation of SVD).
Constructs the Subspace Transformations as the same as NSO
={\textgreater} Target right singular vectors are used as subspace transformation for both source and target data},
author = {Elhadji-Ille-Gado, Nassara and Grall-Maes, Edith and Kharouf, Malika},
booktitle = {2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)},


isbn = {978-1-5386-1418-1},
keywords = {Domain adaptation,approximate SVD,large scale data,low rank matrices,subspace alignment,transfer learning},
month = {dec},
pages = {1006--1010},
publisher = {IEEE},
title = {{Transfer Learning for Large Scale Data Using Subspace Alignment}},

volume = {2018-Janua},
year = {2017}
}
@inproceedings{DBLP:conf/iclr/KipfW17,
abstract = {The vapor pressures and vaporization enthalpies of the three major components of bergamotene oil have been evaluated by correlation gas chromatography. Three major components were identified by GCMS as E $\beta$-caryophyllene, trans $\alpha$-bergamotene and $\alpha$-humulene. Two of the components, E $\beta$-caryophyllene and $\alpha$-humulene were also evaluated using commercial caryophyllene samples. Z-caryophyllene, present in the commercial sample was also evaluated. Two sets of hydrocarbon standards, n-alkanes and a series of relatively unrelated cyclic compounds that included adamantane and two alkyl derivatives, (−) $\alpha$-pinene, diamantane and 1,4-di-t-butylbenzene, were used. Vaporization enthalpies of the targets at T = 298.15 K were reproduced within the specified uncertainties by both sets of standards as were vapor pressures from T = (298.15 to ∼400)K. Above 400 K, vapor pressures diverged. Equations for the evaluation of vapor pressure as a function of temperature are reported for all four compounds investigated.},
author = {Orf, Megan and Kurian, Manu and Espinosa, Lorna and Nelson, Carissa and Simmons, Dan and Chickos, James},
booktitle = {Journal of Chemical Thermodynamics},


issn = {10963626},
keywords = {Correlation gas Chromatography,E $\beta$-caryophyllene,Vapor pressure,Vaporization Enthalpy,Z-caryophyllene,trans $\alpha$-Bergamotene},
pages = {128--136},
title = {{Thermochemical properties of sesquiterpenes in natural products by correlation gas chromatography: Application to bergamotene oil}},

volume = {126},
year = {2018}
}
@article{Abdi2007,
abstract = {The more tests we perform on a set of data, the more likely we are to reject the null hypothesis when it is true (i.e., a “Type I” error). This is a consequence of the logic of hypothesis testing: We reject the null hypothesis if we witness a rare event. But the larger the number of tests, the easier it is to find rare events and therefore the easier it is to make the mistake of thinking that there is an effect when there is none. This problem is called the inflation of the alpha level. In order to be protected fromit, one strategy is to correct the alpha level when performing multiple tests. Making the alpha level more stringent (i.e., smaller) will create less errors, but itmay alsomake it harder to detect real effects.},
author = {Abdi, H},


isbn = {9781412916110},
journal = {Encyclopedia of Measurement and Statistics},
pages = {103--107},
title = {{The Bonferonni and {\v{S}}id{\'{a}}k Corrections for Multiple Comparisons}},
year = {2007}
}
@article{Saralajew2019,
abstract = {Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks has however not yet been studied to the same extent. We therefore present an extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods. In contrast to this, Generalized Matrix LVQ shows a high susceptibility to adversarial attacks, scoring consistently behind all other models. Additionally, our numerical evaluation indicates that increasing the number of prototypes per class improves the robustness of the models.},
archivePrefix = {arXiv},
arxivId = {1902.00577},
author = {Saralajew, Sascha and Holdijk, Lars and Rees, Maike and Villmann, Thomas},

eprint = {1902.00577},

isbn = {9783030196417},
issn = {21945357},
journal = {Advances in Intelligent Systems and Computing},
pages = {189--199},
title = {{Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks}},

volume = {976},
year = {2020}
}
@article{Brandts2014,
author = {Brandts, Jan},

isbn = {978-1-4214-0794-4},
issn = {0862-7940},
journal = {Applications of Mathematics},
month = {jun},
number = {3},
pages = {359--359},
title = {{Book reviews}},

volume = {59},
year = {2014}
}
@article{Kuncheva2008,
abstract = {We address adaptive classification of streaming data in the presence of concept change. An overview of the machine learning approaches reveals a deficit of methods for explicit change detection. Typically, classifier ensembles designed for changing environments do not have a bespoke change detector. Here we take a systematic look at the types of changes in streaming data and at the current approaches and techniques in online classification. Classifier ensembles for change detection are discussed. An example is carried through to illustrate individual and ensemble change detectors for both unlabelled and labelled data. While this paper does not offer ready-made solutions, it outlines possibilities for novel approaches to classification of streaming data.},
author = {Kuncheva, L I},

journal = {Proc. 2nd Workshop {\{}SUEMA{\}} {\{}(ECAI 2008){\}}},
keywords = {Computer},
number = {July},
pages = {5--10},
title = {{Classifier ensembles for detecting concept change in streaming data: {\{}Overview and perspectives{\}}}},

year = {2008}
}
@article{Weiss2016,
abstract = {Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.},
author = {Weiss, Karl and Khoshgoftaar, Taghi M and Wang, DingDing},


issn = {2196-1115},
journal = {Journal of Big Data},
number = {1},
pages = {9},
title = {{A survey of transfer learning}},

volume = {3},
year = {2016}
}
@article{Mustafa2017,
author = {Mustafa, Ahmad M and Ayoade, Gbadebo and Al-naami, Khaled and Khan, Latifur and Hamlen, Kevin W and Thuraisingham, Bhavani and Araujo, Frederico},

isbn = {9781538627150},
journal = {IEEE International Conference on Big Data (BIGDATA) Unsupervised},
keywords = {-stream mining,concept-,deep learning,drift,novel class detection},
pages = {1830--1839},
title = {{Unsupervised Deep Embedding for Novel Class Detection over Data Stream}},
year = {2017}
}
@article{Chen2014,
abstract = {Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals. The popularity of such methods has grown significantly in recent years. However, a limitation of HMC methods is the required gradient computation for simulation of the Hamiltonian dynamical system-such computation is infeasible in problems involving a large sample size or streaming data. Instead, we must rely on a noisy gradient estimate computed from a subset of the data. In this paper, we explore the properties of such a stochastic gradient HMC approach. Surprisingly, the natural implementation of the stochastic approximation can be arbitrarily bad. To address this problem we introduce a variant that uses second-order Langevin dynamics with a friction term that counteracts the effects of the noisy gradient, maintaining the desired target distribution as the invariant distribution. Results on simulated data validate our theory. We also provide an application of our methods to a classification task using neural networks and to online Bayesian matrix factorization.},
archivePrefix = {arXiv},
arxivId = {1402.4102},
author = {Chen, Tianqi and Fox, Emily B. and Guestrin, Carlos},
eprint = {1402.4102},

isbn = {9781634393973},
issn = {1938-7228},
journal = {Proceedings of the 31th International Conference on Machine Learning, ICLR 2014},
title = {{Stochastic Gradient Hamiltonian Monte Carlo}},

volume = {32},
year = {2014}
}
@article{Pinheiro2018,
abstract = {The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution. Most deep learning approaches to domain adaptation consist of two steps: (i) learn features that preserve a low risk on labeled samples (source domain) and (ii) make the features from both domains to be as indistinguishable as possible, so that a classifier trained on the source can also be applied on the target domain. In general, the classifiers in step (i) consist of fully-connected layers applied directly on the indistinguishable features learned in (ii). In this paper, we propose a different way to do the classification, using similarity learning. The proposed method learns a pairwise similarity function in which classification can be performed by computing similarity between prototype representations of each category. The domain-invariant features and the categorical prototype representations are learned jointly and in an end-to-end fashion. At inference time, images from the target domain are compared to the prototypes and the label associated with the one that best matches the image is outputed. The approach is simple, scalable and effective. We show that our model achieves state-of-the-art performance in different unsupervised domain adaptation scenarios.},
author = {Pinheiro, Pedro O.},


isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
number = {i},
pages = {8004--8013},
title = {{Unsupervised Domain Adaptation with Similarity Learning}},
year = {2018}
}
@article{vanDerMaaten.2008,
abstract = {We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualiza-tions produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.},
author = {{Laurens van der Maaten} and {Geoffrey E.}, Hinton},

issn = {02624079},
journal = {Journal of Machine Learning Research},
keywords = {dimensionality reduction,embedding algorithms,manifold learning,multidimensional scaling,visualization},
number = {2210},
pages = {10},
title = {{Visualizing Data using t-SNE}},

volume = {164},
year = {2008}
}
@incollection{Schleif2018,
author = {Schleif, Frank Michael and Raab, Christoph and Tino, Peter},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},


isbn = {9783319977843},
issn = {16113349},
keywords = {Krein space,Non-positive kernel,Sparse model},
pages = {173--183},
title = {{Sparsification of indefinite learning models}},

volume = {11004 LNCS},
year = {2018}
}
@inproceedings{Pan2010,
abstract = {Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain. In this cross-domain sentiment classification setting, to bridge the gap between the domains, we propose a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge. In this way, the clusters can be used to reduce the gap between domain-specific words of the two domains, which can be used to train sentiment classifiers in the target domain accurately. Compared to previous approaches, SFA can discover a robust representation for cross-domain data by fully exploiting the relationship between the domain-specific and domain-independent words via simultaneously co-clustering them in a common latent space. We perform extensive experiments on two real world datasets, and demonstrate that SFA significantly outperforms previous approaches to cross-domain sentiment classification. {\textcopyright} 2010 International World Wide Web Conference Committee (IW3C2).},
address = {New York, New York, USA},
author = {Pan, Sinno Jialin and Ni, Xiaochuan and Sun, Jian-Tao and Yang, Qiang and Chen, Zheng},
booktitle = {Proceedings of the 19th international conference on World wide web - WWW '10},


isbn = {9781605587998},
keywords = {domain adaptation,feature alignment,opinion mining,sentiment classification,transfer learning},
pages = {751},
publisher = {ACM Press},
title = {{Cross-domain sentiment classification via spectral feature alignment}},

year = {2010}
}
@inproceedings{Gong2012,
abstract = {In real-world applications of visual recognition, many factorssuch as pose, illumination, or image qualitycan cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adap- tation techniques aim to correct the mismatch. Existing ap- proaches have concentrated on learning feature represen- tations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are in- trinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that charac- terize changes in geometric and statistical properties from the source to the target domain. Our approach is compu- tationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross- validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our ap- proach over competing methods.},
author = {Gong, Boqing and Shi, Yuan and Sha, Fei and Grauman, Kristen},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},


isbn = {9781467312264},
issn = {10636919},
keywords = {Kernel,Manifolds,Measurement,Principal component analysis,Training,Vectors,Visualization,computational geometry,differential geometry,feature extraction,feature representation learning,geodesic flow kernel models,geometric properties,illumination factor,image classification,image quality factor,image representation,kernel-based method,object recognition,pose factor,statistical analysis,statistical properties,unsupervised domain adaptation,unsupervised learning,visual recognition},
pages = {2066--2073},
title = {{Geodesic flow kernel for unsupervised domain adaptation}},
year = {2012}
}
@article{Bartusiak2019,
abstract = {The complex nature of big data resources requires new structuring methods, especially for textual content. WordNet is a good knowledge source for the comprehensive abstraction of natural language as it offers good implementation for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism, WordNet2Vec, is proposed in this paper. This creates vectors for each word from WordNet. These vectors encapsulate a general position — the role of a given word related to all other words in the given natural language. Any list or set of such vectors contains knowledge about the context of its components within the whole language. This type of word representation can be easily applied to many analytic tasks such as classification or clustering. The usefulness of the WordNet2Vec method is demonstrated in sentiment analysis including the classification of an Amazon opinion text dataset with transfer learning.},
author = {Bartusiak, Roman and Augustyniak, {\L}ukasz and Kajdanowicz, Tomasz and Kazienko, Przemys{\l}aw and Piasecki, Maciej},


issn = {18728286},
journal = {Neurocomputing},
keywords = {Big data,Complex networks,Natural language structuring,Network transformation,Sentiment analysis,Transfer learning,Vectorization,WordNet,WordNet2Vec},
pages = {141--150},
publisher = {Elsevier B.V.},
title = {{WordNet2Vec: Corpora agnostic word vectorization method}},

volume = {326-327},
year = {2019}
}
@article{Kundu2020,
abstract = {There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for real-time adaptation. Devoid of such impractical assumptions, we propose a novel two-stage learning process. 1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. To achieve this, we enhance the model's ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework. 2) In the Deployment stage, the goal is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples. To this end, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighting mechanism, named as Source Similarity Metric (SSM). A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.},
archivePrefix = {arXiv},
arxivId = {2004.04393},
author = {Kundu, Jogendra Nath and Venkat, Naveen and M, Rahul and Babu, R. Venkatesh},
eprint = {2004.04393},

journal = {arXiv},
month = {apr},
title = {{Universal Source-Free Domain Adaptation}},

year = {2020}
}
@incollection{NIPS2019_8517,
author = {Kumagai, Atsutoshi and Iwata, Tomoharu and Fujiwara, Yasuhiro},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {2467--2477},
publisher = {Curran Associates, Inc.},
title = {{Transfer Anomaly Detection by Inferring Latent Domain Representations}},

year = {2019}
}
@incollection{Krizhevsky2012,
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {Pereira, F and Burges, C J C and Bottou, L and Weinberger, K Q},

pages = {1097--1105},
publisher = {Curran Associates, Inc.},
title = {{ImageNet Classification with Deep Convolutional Neural Networks}},

year = {2012}
}
@article{Schleif2020,
author = {Schleif, Frank-michael and Raab, Christoph and Tino, Peter},


issn = {01678655},
journal = {Pattern Recognition Letters},
keywords = {Core sets,Krein space,Large scale indefinite learning,Non-metric learning,Orthogonal matching pursuit,Sparse models,large scale indefinite learning},
month = {jan},
pages = {1--7},
publisher = {Elsevier B.V.},
title = {{Sparsification of core set models in non-metric supervised learning}},

volume = {129},
year = {2020}
}
@article{Wang2019c,
abstract = {Recent work in domain adaptation bridges different domains by adversarially learning a domain-invariant representation that cannot be distinguished by a domain discriminator. Existing methods of adversarial domain adaptation mainly align the global images across the source and target domains. However, it is obvious that not all regions of an image are transferable, while forcefully aligning the untransferable regions may lead to negative transfer. Furthermore, some of the images are significantly dissimilar across domains, resulting in weak image-level transferability. To this end, we present Transferable Attention for Domain Adaptation (TADA), focusing our adaptation model on transferable regions or images. We implement two types of complementary transferable attention: transferable local attention generated by multiple region-level domain discriminators to highlight transferable regions, and transferable global attention generated by single image-level domain discriminator to highlight transferable images. Extensive experiments validate that our proposed models exceed state of the art results on standard domain adaptation datasets.},
author = {Wang, Ximei and Li, Liang and Ye, Weirui and Long, Mingsheng and Wang, Jianmin},


issn = {2374-3468},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
keywords = {Neural Network,SVM,Stacking},
month = {jul},
pages = {5345--5352},
title = {{Transferable Attention for Domain Adaptation}},

volume = {33},
year = {2019}
}
@article{Chen2020,
abstract = {Many text classification tasks are domain-dependent, and various domain adaptation approaches have been proposed to predict unlabeled data in a new domain. Domain-adversarial neural networks (DANN) and their variants have been used widely recently and have achieved promising results for this problem. However, most of these approaches assume that the label proportions of the source and target domains are similar, which rarely holds in most real-world scenarios. Sometimes the label shift can be large and the DANN fails to learn domain-invariant features. In this study, we focus on unsupervised domain adaptation of text classification with label shift and introduce a domain adversarial network with label proportions estimation (DAN-LPE) framework. The DAN-LPE simultaneously trains a domain adversarial net and processes label proportions estimation by the confusion of the source domain and the predictions of the target domain. Experiments show the DAN-LPE achieves a good estimate of the target label distributions and reduces the label shift to improve the classification performance.},
archivePrefix = {arXiv},
arxivId = {2003.07444},
author = {Chen, Zhuohao and Karan, Singla and Atkins, David C. and Imel, Zac E and Narayanan, Shrikanth},
eprint = {2003.07444},

title = {{A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification}},

year = {2020}
}
@article{Kitayama.2011,
author = {Kitayama, Satoshi and Yamazaki, Koetsu and Arakawa, Masao},


issn = {1568-4946},
journal = {Applied Soft Computing},
keywords = {Gaussian Kernel,LS-SVM,RBF network,Width},
number = {8},
pages = {4726--4737},
title = {{Simple Estimate of the Width in the Gaussian Kernel with Adaptive Scaling Technique}},
volume = {11},
year = {2012}
}
@article{Zhong2018,
abstract = {Transfer learning has been found helpful at enhancing the target domain's learning process by transferring useful knowledge from other different but related source domains. In many applications, however, collecting and labeling target information is not only very difficult but also expensive. At the same time, considerable prior experience in this regard exists in other application domains. This paper proposes a feature-based transfer learning method based on distribution similarity that aims at the partial overlap of features between two domains. The non-overlapping features are completed by leveraging the distribution similarity of other features within the source domain. Features of the two domains are then reweighted in accordance with the distribution similarity between the source and target domains. This, in turn, decreases the distribution discrepancy between the two domains, therefore achieving the desired feature transfer. Results of the experiments performed on Facebook and Sina Microblog data sets demonstrate that the proposed method is capable of effectively enhancing the accuracy of the prediction function.},
author = {Zhong, Xiaofeng and Guo, Shize and Shan, Hong and Gao, Liang and Xue, Di and Zhao, Nan},


issn = {2169-3536},
journal = {IEEE Access},
keywords = {Distribution similarity,KL divergence,feature transfer,transfer learning},
pages = {35551--35557},
publisher = {IEEE},
title = {{Feature-Based Transfer Learning Based on Distribution Similarity}},

volume = {6},
year = {2018}
}
@article{Yao2015,
abstract = {{\textcopyright} 2015 IEEE. In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation framework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant low-dimensional structures across domains to correct data distribution mismatch and leverages available unlabeled target examples to exploit the underlying intrinsic information in the target domain. Specifically, SDASL conducts the learning by simultaneously minimizing the classification error, preserving the structure within and across domains, and restricting similarity defined on unlabeled target examples. Encouraging results are reported for two challenging domain transfer tasks (including image-to-image and image-to-video transfers) on several standard datasets in the context of both image object recognition and video concept detection.},
author = {Yao, Ting and Pan, Yingwei and Ngo, Chong Wah and Li, Houqiang and Mei, Tao},


isbn = {9781467369640},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {2142--2150},
title = {{Semi-supervised Domain Adaptation with Subspace Learning for visual recognition}},
volume = {07-12-June},
year = {2015}
}
@article{Zambon2018,
abstract = {Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains. Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes. The methodology is general and considers a process generating attributed graphs with a variable number of vertices/edges, without the need to assume one-to-one correspondence between vertices at different time steps. The methodology acts by embedding every graph of the stream into a vector domain, where a conventional multivariate change detection procedure can be easily applied. We ground the soundness of our proposal by proving several theoretical results. In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings. Experimental results are contrasted with respect to suitable baseline methods, demonstrating the effectiveness of our approach.},
archivePrefix = {arXiv},
arxivId = {1706.06941},
author = {Zambon, Daniele and Alippi, Cesare and Livi, Lorenzo},

eprint = {1706.06941},

issn = {21622388},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
keywords = {Anomaly detection,attributed graph,change detection,concept drift,dynamic/evolving graph,embedding,graph matching,stationarity},
number = {11},
pages = {5592--5605},
publisher = {IEEE},
title = {{Concept Drift and Anomaly Detection in Graph Streams}},
volume = {29},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/MouMYLX0J16,
abstract = {Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.},
author = {Mou, Lili and Meng, Zhao and Yan, Rui and Li, Ge and Xu, Yan and Zhang, Lu and Jin, Zhi},
booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural
          Language Processing},

editor = {Su, Jian and Carreras, Xavier and Duh, Kevin},

isbn = {978-1-945626-25-8},
pages = {479--489},
publisher = {The Association for Computational Linguistics},
title = {{How Transferable are Neural Networks in NLP Applications?}},

year = {2016}
}
@inproceedings{7486497,
author = {Liu, P and Yang, P and Huang, K and Tan, T},
booktitle = {2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)},


issn = {2327-0985},
keywords = {Adaptation models,Benchmark testing,Computational modeling,Dictionaries,Object recognition,Pattern recognition,Visualization,disturbance elimination,domain-invariant dictionary,image classification,image representation,learned dictionary,source domain,subspace alignment technique,target domain,uniform low-rank representation,unsupervised learning,unsupervised visual domain adaptation},
month = {nov},
pages = {216--220},
title = {{Uniform low-rank representation for unsupervised visual domain adaptation}},
year = {2015}
}
@inproceedings{DBLP:conf/cvpr/TsaiYW16,
abstract = {While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. This is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. The effectiveness of our proposed method can be successfully verified.},
author = {Tsai, Yao Hung Hubert and Yeh, Yi Ren and Wang, Yu Chiang Frank},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},


isbn = {9781467388504},
issn = {10636919},
pages = {5081--5090},
title = {{Learning cross-domain landmarks for heterogeneous domain adaptation}},

volume = {2016-Decem},
year = {2016}
}
@article{Lampert2014,
abstract = {We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.},
author = {Lampert, Christoph H. and Nickisch, Hannes and Harmeling, Stefan},


issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Object recognition,vision and scene understanding},
month = {mar},
number = {3},
pages = {453--465},
publisher = {IEEE},
title = {{Attribute-Based Classification for Zero-Shot Visual Object Categorization}},

volume = {36},
year = {2014}
}
@article{Luo2020,
abstract = {Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, there are two issues to be further explored. First, the hard-assigned pseudo labels on the target domain are risky to the intrinsic data structure. Second, the batch-wise training manner in deep learning limits the description of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability consistently. As to the first problem, this method establishes a probabilistic discriminant criterion on the target domain via soft labels. Further, this criterion is extended to a global approximation scheme for the second issue; such approximation is also memory-saving. The manifold metric alignment is exploited to be compatible with the embedding space. A theoretical error bound is derived to facilitate the alignment. Extensive experiments have been conducted to investigate the proposal and results of the comparison study manifest the superiority of consistent manifold learning framework.},
archivePrefix = {arXiv},
arxivId = {2002.08675},
author = {Luo, You-Wei and Ren, Chuan-Xian and Ge, Pengfei and Huang, Ke-Kun and Yu, Yu-Feng},
eprint = {2002.08675},

journal = {CoRR},
month = {feb},
title = {{Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment}},

year = {2020}
}
@inproceedings{pmlr-v28-kuzborskij13,
abstract = {We consider the transfer learning scenario, where the learner does not have access to the source domain directly, but rather operates on the basis of hypotheses induced from it – the Hypothesis Transfer Learning (HTL) problem. Particularly, we conduct a theoretical analysis of HTL by considering the algorithmic stability of a class of HTL algorithms based on Regularized Least Squares with biased regularization. We show that the relatedness of source and target domains accelerates the convergence of the Leave-One-Out error to the generalization error, thus enabling the use of the Leave-One-Out error to find the optimal transfer parameters, even in the presence of a small training set. In case of unrelated domains we also suggest a theoretically principled way to prevent negative transfer, so that in the limit we recover the performance of the algorithm not using any knowledge from the source domain.},
address = {Atlanta, Georgia, USA},
author = {Kuzborskij, Ilja and Orabona, Francesco},
booktitle = {Proceedings of the 30th International Conference on Machine Learning},
editor = {Dasgupta, Sanjoy and McAllester, David},

number = {3},
pages = {942--950},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Stability and Hypothesis Transfer Learning}},

volume = {28},
year = {2013}
}
@inproceedings{pmlr-v97-zhao19a,
abstract = {Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample exhibits $\backslash$emph{\{}conditional shift{\}}: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of $\backslash$emph{\{}any{\}} domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.},
address = {Long Beach, California, USA},
annote = {Domain Adaptation by learning an invariant representation and an classifier on source domain is not sufficient.

It is proposed by the fact that the lambda constrained on the upper bound in Equation 1 (Biliter) can be abritarly large.
the lambda parameter is the joint risk of the optimal hypothesis h*.
Therefore complety aligning the distributions may not help in domain adaptation.

New upper bound based on the distance between the labeling functino from source and target domains.
New lower bound based on the difference on the Jenson Shannon metric of label distribution vs feature distribution.
The bound also helps for seperate transformations of source and target respectivly.},
author = {Zhao, Han and Combes, Remi Tachet Des and Zhang, Kun and Gordon, Geoffrey},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},

pages = {7523--7532},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{On Learning Invariant Representations for Domain Adaptation}},

volume = {97},
year = {2019}
}
@incollection{NIPS2019_9151,
author = {Micaelli, Paul and Storkey, Amos J},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {9547--9557},
publisher = {Curran Associates, Inc.},
title = {{Zero-shot Knowledge Transfer via Adversarial Belief Matching}},

year = {2019}
}
@article{Blitzer2011,
abstract = {Domain adaptation algorithms address a key issue in applied machine learning: How can we train a system under a source distribution but achieve high performance under a different target distribution? We tackle this question for divergent distributions where crucial predictive target features may not even have support under the source distribution. In this setting, the key intuition is that that if we can link target-specific features to source features, we can learn effectively using only source labeled data. We formalize this intuition, as well as the assumptions under which such coupled learning is possible. This allows us to give finite sample target error bounds (using only source training data) and an algorithm which performs at the state-of-the-art on two natural language processing adaptation tasks which are characterized by novel target features. Copyright 2011 by the authors.},
author = {Blitzer, John and Foster, Dean and Kakade, Sham},

issn = {1533-7928},
journal = {Journal of Machine Learning Research},
pages = {173--181},
title = {{Domain adaptation with coupled subspaces}},

volume = {15},
year = {2011}
}
@article{Li2017,
abstract = {In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient descent method for the principal component analysis. Oja's iteration maintains a running estimate of the true principal component from streaming data and enjoys less temporal and spatial complexities. We show that the Oja's iteration for the top eigenvector generates a continuous-state discrete-time Markov chain over the unit sphere. We characterize the Oja's iteration in three phases using diffusion approximation and weak convergence tools. Our three-phase analysis further provides a finite-sample error bound for the running estimate, which matches the minimax information lower bound for principal component analysis under the additional assumption of bounded samples.},
archivePrefix = {arXiv},
arxivId = {1808.09645},
author = {Li, Chris Junchi and Wang, Mengdi and Liu, Han and Zhang, Tong},
eprint = {1808.09645},

journal = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems},
number = {Nips},
pages = {1--11},
title = {{Diffusion Approximations for Online Principal Component Estimation and Global Convergence}},

year = {2018}
}
@incollection{NIPS2011_4433,
author = {Chen, Minmin and Weinberger, Kilian Q and Blitzer, John},
booktitle = {Advances in Neural Information Processing Systems 24},
editor = {Shawe-Taylor, J and Zemel, R S and Bartlett, P L and Pereira, F and Weinberger, K Q},

pages = {2456--2464},
publisher = {Curran Associates, Inc.},
title = {{Co-Training for Domain Adaptation}},

year = {2011}
}
@article{SchleifGT16,
abstract = {Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval. Lacking an underlying vector space, the data are given as pairwise similarities only. The few algorithms available for such data do not scale to larger datasets. Focusing on probabilistic batch classifiers, the Indefinite Kernel Fisher Discriminant (iKFD) and the Probabilistic Classification Vector Machine (PCVM) are both effective algorithms for this type of data but, with cubic complexity. Here we propose an extension of iKFD and PCVM such that linear runtime and memory complexity is achieved for low rank indefinite kernels. Employing the Nystr$\backslash$"om approximation for indefinite kernels, we also propose a new almost parameter free approach to identify the landmarks, restricted to a supervised learning problem. Evaluations at several larger similarity data from various domains show that the proposed methods provides similar generalization capabilities while being easier to parametrize and substantially faster for large scale data.},
archivePrefix = {arXiv},
arxivId = {1604.02264},
author = {Schleif, Frank-Michael and Gisbrecht, Andrej and Tino, Peter},
eprint = {1604.02264},

journal = {CoRR},
title = {{Probabilistic classifiers with low rank indefinite kernels}},

volume = {abs/1604.0},
year = {2016}
}
@article{Taylor2009,
abstract = {The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work.},
archivePrefix = {arXiv},
arxivId = {0803.0476},
author = {Taylor, Matthew E and Stone, Peter},

eprint = {0803.0476},

isbn = {15324435},
issn = {15324435},
journal = {Journal of Machine Learning Research},
keywords = {1,1998,actions with goal,example,leaning agents take sequential,maximizing a reward,multi task learning,problems,reinforcement learning,rl,signal,sutton barto,transfer learning,transfer learning objectives,which may time delayed},
pages = {1633--1685},
pmid = {260529900010},
title = {{Transfer Learning for Reinforcement Learning Domains : A Survey}},

volume = {10},
year = {2009}
}
@inproceedings{DBLP:conf/nips/RudiCCR18,
abstract = {Leverage score sampling provides an appealing way to perform approximate computations for large matrices. Indeed, it allows to derive faithful approximations with a complexity adapted to the problem at hand. Yet, performing leverage scores sampling is a challenge in its own right requiring further approximations. In this paper, we study the problem of leverage score sampling for positive definite matrices defined by a kernel. Our contribution is twofold. First we provide a novel algorithm for leverage score sampling and second, we exploit the proposed method in statistical learning by deriving a novel solver for kernel ridge regression. Our main technical contribution is showing that the proposed algorithms are currently the most efficient and accurate for these problems.},
archivePrefix = {arXiv},
arxivId = {1810.13258},
author = {Rudi, Alessandro and Calandriello, Daniele and Carratino, Luigi and Rosasco, Lorenzo},
booktitle = {Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montr{\'{e}}al, Canada.},
eprint = {1810.13258},

pages = {5677--5687},
title = {{On Fast Leverage Score Sampling and Optimal Learning}},

year = {2018}
}
@article{Zou2017,
abstract = {A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n observed sequences out of which s anomalous sequences are to be detected. Each typical sequence consists of m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence consists of m i.i.d. samples drawn from a distribution q that is distinct from p. The distributions p and q are assumed to be unknown in advance. Distribution-free tests are constructed by using the maximum mean discrepancy as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space. The probability of error is bounded as a function of the sample size m, the number s of anomalous sequences, and the number n of sequences. It is shown that with s known, the constructed test is exponentially consistent if m is greater than a constant factor of n, for any p and q, whereas with s unknown, m should have an order strictly greater than n. Furthermore, it is shown that no test can be consistent for arbitrary p and q if m is less than a constant factor of n. Thus, the order-level optimality of the proposed test is established. Numerical results are provided to demonstrate that the proposed tests outperform (or perform as well as) tests based on other competitive approaches under various cases.},
archivePrefix = {arXiv},
arxivId = {1405.2294},
author = {Zou, Shaofeng and Liang, Yingbin and Poor, H. Vincent and Shi, Xinghua},

eprint = {1405.2294},

issn = {1053587X},
journal = {IEEE Transactions on Signal Processing},
keywords = {Anomalous hypothesis testing,consistency,distribution-free tests,maximum mean discrepancy (MMD)},
number = {21},
pages = {5785--5797},
title = {{Nonparametric Detection of Anomalous Data Streams}},
volume = {65},
year = {2017}
}
@book{horn2012matrix,
author = {Horn, Roger A and Johnson, Charles R},
publisher = {Cambridge university press},
title = {{Matrix analysis}},
year = {2012}
}
@inproceedings{Haque.2014,
abstract = {Big Data Stream mining has some inherent challenges which are not present in traditional data mining. Not only Big Data Stream receives large volume of data continuously, but also it may have different types of features. Moreover, the concepts and features tend to evolve throughout the stream. Traditional data mining techniques are not sufficient to address these challenges. In our current work, we have designed a multi-tiered ensemble based method HSMiner to address aforementioned challenges to label instances in an evolving Big Data Stream. However, this method requires building large number of AdaBoost ensembles for each of the numeric features after receiving each new data chunk which is very costly. Thus, HSMiner may face scalability issue in case of classifying Big Data Stream. To address this problem, we propose three approaches to build these large number of AdaBoost ensembles using MapReduce based parallelism. We compare each of these approaches from different aspects of design. We also empirically show that, these approaches are very useful for our base method to achieve significant scalability and speedup.},
author = {Haque, Ahsanul and Parker, Brandon and Khan, Latifur and Thuraisingham, Bhavani},
booktitle = {IEEE International Conference on Cloud Computing, CLOUD},


isbn = {9781479950638},
issn = {21596190},
keywords = {Distributed Processing,Evolving Big Data Stream,MapReduce,Scalability},
pages = {570--577},
title = {{Evolving big data stream classification with MapReduce}},

year = {2014}
}
@inproceedings{mikolov-etal-2018-advances,
address = {Miyazaki, Japan},
author = {Mikolov, Tomas and Grave, Edouard and Bojanowski, Piotr and Puhrsch, Christian and Joulin, Armand},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({\{}LREC{\}} 2018)},

publisher = {European Language Resources Association (ELRA)},
title = {{Advances in Pre-Training Distributed Word Representations}},

year = {2018}
}
@article{Tan2011,
abstract = {This paper introduces Streaming Half-Space-Trees (HS-Trees), a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. The model features an ensemble of random HS-Trees, and the tree structure is constructed without any data. This makes the method highly efficient because it requires no model restructuring when adapting to evolving data streams. Our analysis shows that Streaming HS-Trees has constant amortised time complexity and constant memory requirement. When compared with a state-of-the-art method, our method performs favourably in terms of detection accuracy and runtime performance. Our experimental results also show that the detection performance of Streaming HS-Trees is not sensitive to its parameter settings.},
author = {Tan, Swee Chuan and Ting, Kai Ming and Liu, Tony Fei},


isbn = {9781577355120},
issn = {10450823},
journal = {IJCAI International Joint Conference on Artificial Intelligence},
keywords = {Machine Learning},
pages = {1511--1516},
title = {{Fast anomaly detection for streaming data}},
year = {2011}
}
@article{Rozantsev2019,
abstract = {The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expensive or impractical. Domain Adaptation has therefore emerged as a solution to this problem; It leverages annotated data from a source domain, in which it is abundant, to train a classifier to operate in a target domain, in which it is either sparse or even lacking altogether. In this context, the recent trend consists of learning deep architectures whose weights are shared for both domains, which essentially amounts to learning domain invariant features. Here, we show that it is more effective to explicitly model the shift from one domain to the other. To this end, we introduce a two-stream architecture, where one operates in the source domain and the other in the target domain. In contrast to other approaches, the weights in corresponding layers are related but not shared. We demonstrate that this both yields higher accuracy than state-of-the-art methods on several object recognition and detection tasks and consistently outperforms networks with shared weights in both supervised and unsupervised settings.},
author = {Rozantsev, Artem and Salzmann, Mathieu and Fua, Pascal},


issn = {19393539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Domain adaptation,deep learning},
number = {4},
pages = {801--814},
publisher = {IEEE},
title = {{Beyond Sharing Weights for Deep Domain Adaptation}},
volume = {41},
year = {2019}
}
@article{Alpaydm.1999,
abstract = {Dietterich (1998) reviews five statistical tests and proposes the 5 x 2 cv t test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5 x 2 cv t test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5 x 2 cv F test, that combines multiple statistics to get a more robust test. Simulation results show that this combined version of the test has lower type I error and higher power than 5 x 2 cv proper.},
author = {Alpaydin, Ethem},


issn = {0899-7667},
journal = {Neural Computation},
keywords = {Algorithme apprentissage,Algoritmo aprendizaje,Clasificaci{\'{o}}n supervisada,Classification supervis{\'{e}}e,Learning algorithm,Neural network,Red neuronal,R{\'{e}}seau neuronal,Statistical test,Supervised classification,Test estad{\'{i}}stico,Test statistique},
number = {8},
pages = {1885--1892},
title = {{Combined 5 x 2 cv F test for comparing supervised classification learning algorithms}},
volume = {11},
year = {1999}
}
@inproceedings{Dai2007,
abstract = {Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund {\&} Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.},
address = {New York, New York, USA},
author = {Dai, Wenyuan and Yang, Qiang and Xue, Gui-Rong and Yu, Yong},
booktitle = {Proceedings of the 24th international conference on Machine learning - ICML '07},


isbn = {9781595937933},
issn = {1595937935},
pages = {193--200},
pmid = {26886975},
publisher = {ACM Press},
title = {{Boosting for transfer learning}},

year = {2007}
}
@article{Losing2015,
abstract = {—We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learn-ing by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on a subset of samples. We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories.},
author = {Losing, Viktor and Hammer, Barbara and Wersing, Heiko},


isbn = {9781479919604},
journal = {Proceedings of the International Joint Conference on Neural Networks},
keywords = {Robots,Testing},
number = {2},
title = {{Interactive online learning for obstacle classification on a mobile robot}},
volume = {2015-Septe},
year = {2015}
}
@article{Donahue2014,
abstract = {We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re- purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimenta-tion with deep representations across a range of visual concept learning paradigms.},
archivePrefix = {arXiv},
arxivId = {1310.1531},
author = {Donahue, Jeff and Jia, Yangqing and Vinyals, Oriol and Hoffman, Judy and Zhang, Ning and Tzeng, Eric and Darrell, Trevor},
eprint = {1310.1531},

isbn = {9781634393973},
journal = {31st International Conference on Machine Learning, ICML 2014},
pages = {988--996},
title = {{DeCAF: A deep convolutional activation feature for generic visual recognition}},
volume = {2},
year = {2014}
}
@article{Ruder16,
abstract = {Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.},
archivePrefix = {arXiv},
arxivId = {1609.04747},
author = {Ruder, Sebastian},
eprint = {1609.04747},

journal = {CoRR},
title = {{An overview of gradient descent optimization algorithms}},

volume = {abs/1609.0},
year = {2016}
}
@article{Hammer2002,
author = {Hammer, B and Villmann},

journal = {Neural Networks},
keywords = {adaptive metric,clustering,learning vector quantization,relevance determination},
pages = {1059--1068},
title = {{Generalized relevance learning vector quantization. Neural Networks}},
volume = {15},
year = {2002}
}
@article{TheckelJoy2019,
abstract = {Experimental optimization is prevalent in many areas of artificial intelligence including machine learning. Conventional methods like grid search and random search can be computationally demanding. Over the recent years, Bayesian optimization has emerged as an efficient technique for global optimization of black-box functions. However, a generic Bayesian optimization algorithm suffers from a “cold start” problem. It may struggle to find promising locations in the initial stages. We propose a novel transfer learning method for Bayesian optimization where we leverage the knowledge from an already completed source optimization task for the optimization of a target task. Assuming both the source and target functions lie in some proximity to each other, we model source data as noisy observations of the target function. The level of noise models the proximity or relatedness between the tasks. We provide a mechanism to compute the noise level from the data to automatically adjust for different relatedness between the source and target tasks. We then analyse the convergence properties of the proposed method using two popular acquisition functions. Our theoretical results show that the proposed method converges faster than a generic no-transfer Bayesian optimization. We demonstrate the effectiveness of our method empirically on the tasks of tuning the hyperparameters of three different machine learning algorithms. In all the experiments, our method outperforms state-of-the-art transfer learning and no-transfer Bayesian optimization methods.},
author = {{Theckel Joy}, Tinu and Rana, Santu and Gupta, Sunil and Venkatesh, Svetha},


isbn = {9783319317526},
issn = {09574174},
journal = {Expert Systems with Applications},
keywords = {Bayesian optimization,Gaussian process,Transfer learning},
pages = {656--672},
publisher = {Elsevier Ltd},
title = {{A flexible transfer learning framework for Bayesian optimization with convergence guarantee}},

volume = {115},
year = {2019}
}
@article{Kornblith2018,
abstract = {Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy ({\$}r = 0.99{\$} and {\$}0.96{\$}, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield penultimate layer features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.},
archivePrefix = {arXiv},
arxivId = {1805.08974},
author = {Kornblith, Simon and Shlens, Jonathon and Le, Quoc V.},
eprint = {1805.08974},

month = {may},
pages = {2661--2671},
title = {{Do Better ImageNet Models Transfer Better?}},

year = {2018}
}
@article{Garrison2004,
abstract = {The purpose of this paper is to provide a discussion of the transformative potential of blended learning in the context of the challenges facing higher education. Based upon a description of blended learning, its potential to support deep and meaningful learning is discussed. From here, a shift to the need to rethink and restructure the learning experience occurs and its transformative potential is analyzed. Finally, administrative and leadership issues are addressed and the outline of an action plan to implement blended learning approaches is presented. The conclusion is that blended learning is consistent with the values of traditional higher education institutions and has the proven potential to enhance both the effectiveness and efficiency of meaningful learning experiences. {\textcopyright} 2004 Elsevier Inc. All rights reserved.},
author = {Garrison, D. Randy and Kanuka, Heather},


isbn = {1096-7516},
issn = {10967516},
journal = {Internet and Higher Education},
keywords = {Action plans,Blended learning,Communities of inquiry,Higher education,Higher-order learning,Leadership,Transformation},
number = {2},
pages = {95--105},
pmid = {13434395},
title = {{Blended learning: Uncovering its transformative potential in higher education}},
volume = {7},
year = {2004}
}
@book{ErpenbeckJohnSauterSimonSauter,
abstract = {Abstract Die Autoren beschreiben die Deutungen des Begriffes E-Learning und erl{\"{a}}utern die Rahmenbedingungen sowie die Elemente von E-Learning Arrangements. Sie entwickeln ein praxiserprobtes Blended Learning Arrangement, das problemorientierte Workshops mit selbstgesteuertem Lernen auf der Basis von Web Based Trainings oder Lernvideos verkn{\"{u}}pft. Erfolgsfaktoren f{\"{u}}r E-Learning und Blended Learning Arrangements werden analysiert, eine zuverl{\"{a}}ssige Konzeption der Lernbegleitung wird entwickelt. Die Autoren leiten zudem auf Basis ihrer langj{\"{a}}hrigen Projekterfahrung Fallstudien f{\"{u}}r E-Learning und Blended Learning in Verbindung mit Social Learning ab, welche sich durch eine konsequente Bedarfsorientierung, Effizienz und Wirtschaftlichkeit auszeichnen. {\~{}} Der Inhalt {\textperiodcentered}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}} Die Lernlandschaft wandelt sich {\textperiodcentered}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}} Wissensaufbau mit E-Learning {\textperiodcentered}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}} Qualifizierung mit Blended Learning {\textperiodcentered}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}} Fallstudien {\~{}} Die Zielgruppen {\textperiodcentered}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}} Fach- und F{\"{u}}hrungskr{\"{a}}fte im Personalbereich, Bildungsplaner und Trainer {\textperiodcentered}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}}{\~{}} Studierende und Dozierende der Betriebswirtschaftslehre und der P{\"{a}}dagogik {\~{}} Die Autoren Prof. Dr. John Erpenbeck lehrt Kompetenzentwicklung an der SIBE - School of International Business and Entrepreneurship - in Berlin. Simon Sauter ist Gesch{\"{a}}ftsf{\"{u}}hrer und Leiter der Medienentwicklung der Blended Solutions GmbH in Berlin. Prof. Dr. Werner Sauter ist wissenschaftlicher Leiter der Blended Solutions GmbH und begleitet Unternehmen und Bildungsanbieter bei der Konzipierung und Implementierung innovativer Lernarrangements},
address = {Wiesbaden},
author = {Erpenbeck, John and Sauter, Simon and Sauter, Werner},


isbn = {978-3-658-10174-9},
publisher = {Springer Fachmedien Wiesbaden},
series = {essentials},
title = {{E-Learning und Blended Learning}},

year = {2015}
}
@inproceedings{Zhang_2019_CVPR,
author = {Zhang, Yabin and Tang, Hui and Jia, Kui and Tan, Mingkui},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

title = {{Domain-Symmetric Networks for Adversarial Domain Adaptation}},
year = {2019}
}
@article{Chen2013,
abstract = {Copyright {\textcopyright} 2013 ACM. We present novel, efficient, model based kernels for time series data rooted in the reservoir computation framework. The kernels are implemented by fitting reservoir models sharing the same fixed deterministically constructed state transition part to individual time series. The proposed kernels can naturally handle time series of different length without the need to specify a parametric model class for the time series. Compared with most time series kernels, our kernels are computationally efficient. We show how the model distances used in the kernel can be calculated analytically or efficiently estimated. The experimental results on synthetic and benchmark time series classification tasks confirm the efficiency of the proposed kernel in terms of both generalization accuracy and computational speed. This paper also investigates on-line reservoir kernel construction for extremely long time series.},
author = {Chen, Huanhuan and Tang, Fengzhen and Tino, Peter and Yao, Xin},


isbn = {9781450321747},
issn = {9781450321747},
journal = {Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13},
keywords = {kernel methods,reservoir computing,time series classi-},
pages = {392},
title = {{Model-based kernel for efficient time series analysis}},

year = {2013}
}
@article{Madry2017,
abstract = {OBJECTIVE Deep hypothermic circulatory arrest is a useful adjunct for treating complex aneurysms. Decreased cerebral metabolism and resultant ischemic tolerance create an environment suitable for devascularizing high-risk lesions. However, the advent of modern imaging modalities, innovative cerebral revascularization strategies, and the emergence of endovascular stenting and coiling limit the number of aneurysms requiring this surgical intervention. We present 66 patients with intracranial aneurysms who underwent surgical clipping under deep hypothermic arrest and attempt to identify patients well-suited for this procedure. METHODS This study was conducted during a 15-year period and examined patients with aneurysms of the anterior and posterior cerebral circulation. Demographics, aneurysm characteristics, and surgical factors were evaluated as predictors of functional outcome. RESULTS Patient age and the duration of cardiac arrest were independent predictors of early clinical outcome (P {\textless} 0.05). Our experience suggests that the ideal patient is younger than 60 years old and harbors few medical comorbidities. Individuals with large aneurysms of the anterior communicating artery, internal carotid artery bifurcation, posterior inferior cerebellar artery, midbasilar, or vertebral arteries and with an absence of thrombosis and calcium may be most likely to experience favorable outcomes. Circulatory arrest should not exceed 30 minutes. Postoperative computed tomographic scanning and timely anesthetic emergence allow for early detection of hemorrhage. Complete dissection of the aneurysm before bypass and avoiding extreme hypothermia yield a low incidence of life-threatening postoperative hematomas. CONCLUSION Hypothermic circulatory arrest is a useful technique for neuroprotection during the clipping of complex cerebral aneurysms. This procedure, however, has several associated risks. Patient factors, pathoanatomic characteristics, and surgical parameters may be used to guide patient selection.},
archivePrefix = {arXiv},
arxivId = {1706.06083},
author = {Mack, William J. and Ducruet, Andrew F. and Angevine, Peter D. and Komotar, Ricardo J. and Shrebnick, Debra B. and Edwards, Niloo M. and Smith, Craig R. and Heyer, Eric J. and Monyero, Linda and Connolly, E. Sander and Solomon, Robert A.},

eprint = {1706.06083},

isbn = {9781405161251},
issn = {0148396X},
journal = {Neurosurgery},
keywords = {Aneurysm,Circulatory arrest,Hypothermia},
number = {5},
pages = {815--824},
pmid = {17460516},
title = {{Deep hypothermic circulatory arrest for complex cerebral aneurysms: Lessons learned}},

volume = {60},
year = {2007}
}
@article{VanLaarhoven2017,
abstract = {Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related domain. We cast domain adaptation as the problem of finding stable labels for target examples. A new definition of label stability is proposed, motivated by a generalization error bound for large margin linear classifiers: a target labeling is stable when, with high probability, a classifier trained on a random subsample of the target with that labeling yields the same labeling. We find stable labelings using a random walk on a directed graph with transition probabilities based on labeling stability. The majority vote of those labelings visited by the walk yields a stable label for each target example. The resulting domain adaptation algorithm is strikingly easy to implement and apply: It does not rely on data transformations, which are in general computational prohibitive in the presence of many input features, and does not need to access the source data, which is advantageous when data sharing is restricted. By acting on the original feature space, our method is able to take full advantage of deep features from external pre-trained neural networks, as demonstrated by the results of our experiments.},
annote = {Find pseudo labels by random walk and interatlivly updating the labels of a classifier based on a bootstrap sample},
archivePrefix = {arXiv},
arxivId = {1706.05335},
author = {van Laarhoven, Twan and Marchiori, Elena},
eprint = {1706.05335},

journal = {CoRR},
month = {jun},
title = {{Unsupervised Domain Adaptation with Random Walks on Target Labelings}},

year = {2017}
}
@article{Herman2013,
abstract = {At SODAE10, Agarwal and Sharathkumar presented a streaming algorithm for approximating the minimum enclosing ball of a set of points in d-dimensional Euclidean space. Their algorithm requires one pass, uses O(d) space, and was shown to have approximation factor at most (1+3)/2+$\epsilon$≈1.3661. We prove that the same algorithm has approximation factor less than 1.22, which brings us much closer to a (1+2)/2≈1.207 lower bound given by Agarwal and Sharathkumar. We also apply this technique to the dynamic version of the minimum enclosing ball problem (in the non-streaming setting). We give an O(dn)-space data structure that can maintain a 1.22-approximate minimum enclosing ball in O(dlogn) expected amortized time per insertion/deletion. {\textcopyright} 2013 Elsevier B.V.},
author = {Chan, Timothy M. and Pathak, Vinayak},


isbn = {9782343022109},
issn = {09257721},
journal = {Computational Geometry: Theory and Applications},
keywords = {Dynamic algorithms,High dimension,Streaming algorithms},
number = {2 PART A},
pages = {240--247},
publisher = {Elsevier B.V.},
title = {{Streaming and dynamic algorithms for minimum enclosing balls in high dimensions}},

volume = {47},
year = {2014}
}
@article{Yang2020,
abstract = {The conventional classification models implicitly assume that the distributions of data employed for training and test are identical. However, the assumption is rarely valid in many practical applications. In order to alleviate the difference between the distributions of the training and test sets, in this paper, we propose a regularized subspace learning framework based on the low-rank representation technique for unsupervised domain adaptation. Specifically, we introduce a regularization term of the subspace projection matrix to deal with the ill-conditioned problem and obtain a unique numerical solution. Meanwhile, we impose a structured sparsity-inducing regularizer on the error term so that the proposed method can filter out the outlier information, and therefore improve the performance. The extensive comparison experiments on benchmark data sets demonstrate the effectiveness of the proposed method.},
author = {Yang, Liran and Men, Min and Xue, Yiming and Zhong, Ping},


issn = {1380-7501},
journal = {Multimedia Tools and Applications},
keywords = {Domain adaptation,Low-rank representation,Regularization,Robust,Subspace learning},
month = {jan},
number = {3-4},
pages = {3031--3047},
publisher = {Multimedia Tools and Applications},
title = {{Low-rank representation-based regularized subspace learning method for unsupervised domain adaptation}},

volume = {79},
year = {2020}
}
@article{Ralaivola2005,
abstract = {Increased availability of large repositories of chemical compounds is creating new challenges and opportunities for the application of machine learning methods to problems in computational chemistry and chemical informatics. Because chemical compounds are often represented by the graph of their covalent bonds, machine learning methods in this domain must be capable of processing graphical structures with variable size. Here, we first briefly review the literature on graph kernels and then introduce three new kernels (Tanimoto, MinMax, Hybrid) based on the idea of molecular fingerprints and counting labeled paths of depth up to d using depth-first search from each possible vertex. The kernels are applied to three classification problems to predict mutagenicity, toxicity, and anti-cancer activity on three publicly available data sets. The kernels achieve performances at least comparable, and most often superior, to those previously reported in the literature reaching accuracies of 91.5{\%} on the Mutag dataset, 65-67{\%} on the PTC (Predictive Toxicology Challenge) dataset, and 72{\%} on the NCI (National Cancer Institute) dataset. Properties and tradeoffs of these kernels, as well as other proposed kernels that leverage 1D or 3D representations of molecules, are briefly discussed. {\textcopyright} 2005 Elsevier Ltd. All rights reserved.},
author = {Ralaivola, Liva and Swamidass, Sanjay J. and Saigo, Hiroto and Baldi, Pierre},


issn = {08936080},
journal = {Neural Networks},
keywords = {Activity,Chemical informatics,Computational chemistry,Convolution kernels,Drug design,Graph kernels,Kernel methods,Recursive neural networks,Spectral kernels,Toxicity},
month = {oct},
number = {8},
pages = {1093--1110},
title = {{Graph kernels for chemical informatics}},

volume = {18},
year = {2005}
}
@inproceedings{Ediger.2010,
abstract = {We present a new approach for parallel massive graph analysis of streaming, temporal data with a dynamic and extensible representation. Handling the constant stream of new data from health care, security, business, and social network applications requires new algorithms and data structures. We examine data structure and algorithm trade-offs that extract the parallelism necessary for high-performance updating analysis of massive graphs. Static analysis kernels often rely on storing input data in a specific structure. Maintaining these structures for each possible kernel with high data rates incurs a significant performance cost. A case study computing clustering coefficients on a general-purpose data structure demonstrates incremental updates can be more efficient than global recomputation. Within this kernel, we compare three methods for dynamically updating local clustering coefficients: a brute-force local recalculation, a sorting algorithm, and our new approximation method using a Bloom filter. On 32 processors of a Cray XMT with a synthetic scale-free graph of 224 {\^{A}}¿ 16 million vertices and 229 {\^{A}}¿ 537 million edges, the brute-force method processes a mean of over 50 000 updates per second and our Bloom filter approaches 200 000 updates per second.},
author = {Ediger, David and Jiang, Karl and Riedy, Jason and Bader, David A.},
booktitle = {Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010},


isbn = {9781424465347},
keywords = {approximation theory;data analysis;data structures},
month = {apr},
pages = {1--8},
title = {{Massive streaming data analytics: A case study with clustering coefficients}},
year = {2010}
}
@article{Hu2016,
abstract = {{\textcopyright} 2015 IEEE. Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML where the output of both the hidden layers and the top layer are optimized jointly. Experimental results on cross-dataset face verification and person re-identification validate the effectiveness of the proposed methods.},
author = {Hu, Junlin and Lu, Jiwen and Tan, Yap Peng and Zhou, Jie},


issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Deep metric learning,deep transfer metric learning,face verification,person reidentification,transfer learning},
number = {12},
pages = {5576--5588},
title = {{Deep Transfer Metric Learning}},
volume = {25},
year = {2016}
}
@inproceedings{Crammer2002,
abstract = {Prototypes based algorithms are commonly used to reduce the computational$\backslash$ncomplexity of Nearest-Neighbour (NN) classiers. In this paper we discuss$\backslash$ntheoretical and algorithmical aspects of such algorithms. On the theory side, we$\backslash$npresent margin based generalization bounds that suggest that these kinds of$\backslash$nclassiers can be more accurate then the 1-NN rule. Furthermore, we derived a$\backslash$ntraining algorithm that selects a good set of prototypes using large margin$\backslash$nprinciples. We also show that the 20 years old Learning Vector Quantization$\backslash$n(LVQ) algorithm emerges naturally from our framework.},
author = {Crammer, Koby and Gilad-Bachrach, Ran and Navot, Amir and Tishby, Naftali},
booktitle = {Neural Information Processing Systems (NIPS)},

isbn = {0262025507 | 9780262025508},
issn = {10495258},
pages = {462--469},
title = {{Margin Analysis of The Lvq Algorithm}},

year = {2002}
}
@article{Montiel2018,
abstract = {Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality is enforced by complying with PEP8 guidelines and using continuous integration and automatic testing. The source code is publicly available at https://github.com/scikit-multiflow/scikit-multiflow.},
archivePrefix = {arXiv},
arxivId = {1807.04662},
author = {Montiel, Jacob and Read, Jesse and Bifet, Albert and Abdessalem, Talel},
eprint = {1807.04662},

journal = {Journal of Machine Learning Research},
keywords = {drift detection,machine learning,multi-output,python,stream data},
pages = {1--5},
title = {{Scikit-Multiflow: A Multi-output Streaming Framework}},

volume = {1},
year = {2018}
}
@inproceedings{Duchi2011,
abstract = {We analyze convergence rates of stochastic optimization procedures for non-smooth convex optimization problems. By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates of stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for non-smooth optimization. We give several applications of our results to statistical estimation problems, and provide experimental results that demonstrate the effectiveness of the proposed algorithms. We also describe how a combination of our algorithm with recent work on decentralized optimization yields a distributed stochastic optimization algorithm that is order-optimal.},
archivePrefix = {arXiv},
arxivId = {arXiv:1103.4296v1},
author = {Duchi, John C. and Bartlett, Peter L. and Wainwright, Martin J.},
booktitle = {Proceedings of the IEEE Conference on Decision and Control},

eprint = {arXiv:1103.4296v1},

isbn = {978-1-4673-2066-5},
issn = {01912216},
keywords = {adaptivity,online learning,stochastic convex optimization,subgradient methods},
month = {dec},
pages = {5442--5444},
pmid = {2868127},
publisher = {IEEE},
title = {{Randomized smoothing for (parallel) stochastic optimization}},

volume = {12},
year = {2012}
}
@inproceedings{DBLP:conf/nips/MikolovSCCD13,
abstract = {The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large num- ber of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alterna- tive to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of “Canada” and “Air” cannot be easily combined to obtain “Air Canada”. Motivated by this example,we present a simplemethod for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.},
author = {Mikolov, Tomas and Sutskever, Ilya and Chen, Kai and Corrado, Gregory S and Dean, Jeffrey},
booktitle = {CrossRef Listing of Deleted DOIs},


issn = {0003-6951},
pages = {3111--3119},
title = {{Distributed Representations ofWords and Phrases and their Compositionality}},

volume = {1},
year = {2000}
}
@inproceedings{Li2019b,
abstract = {Adversarial domain adaptation has achieved some success in learning transferable feature representations and reducing distribution discrepancy between source and target domains. However, existing approaches mainly focus on alignment of global source and target distributions without considering complex structures in categories underlying different distributions, resulting in domain confusion and the mix of distinguishable structures. In this paper, we propose an adversarial domain adaptation via category transfer (ADACT) approach for unsupervised domain adaptation (UDA). ADACT first captures multi-category information through training source and target feature generators as well as a label predictor. Secondly, it uses multi-category domain critic networks to category-wisely estimate Wasserstein distances across domains. Then it learns category-invariant feature representations by finely-grained matching different data distributions with the estimated Wasserstein distances. The adaptation can be achieved by the standard back-propagation training approach with this two-step iteration. The effectiveness of ADACT is demonstrated since it outperforms several state-of-the-art UDA methods on common domain adaptation datasets.},
author = {Li, Lusi and He, Haibo and Li, Jie and Yang, Guang},
booktitle = {2019 International Joint Conference on Neural Networks (IJCNN)},


isbn = {978-1-7281-1985-4},
keywords = {adversarial domain adaptation,category-invariant feature representations,complex structures,distribution discrepancy},
month = {jul},
number = {July},
pages = {1--8},
publisher = {IEEE},
title = {{Adversarial Domain Adaptation via Category Transfer}},

volume = {2019-July},
year = {2019}
}
@inproceedings{Kuzborskij.17,
abstract = {We study algorithms for online nonparametric regression that learn the directions along which the regression function is smoother. Our algorithm learns the Mahalanobis metric based on the gradient outer product matrix {\$}\backslashboldsymbol{\{}G{\}}{\$} of the regression function (automatically adapting to the effective rank of this matrix), while simultaneously bounding the regret ---on the same data sequence--- in terms of the spectrum of {\$}\backslashboldsymbol{\{}G{\}}{\$}. As a preliminary step in our analysis, we extend a nonparametric online learning algorithm by Hazan and Megiddo enabling it to compete against functions whose Lipschitzness is measured with respect to an arbitrary Mahalanobis metric.},
archivePrefix = {arXiv},
arxivId = {1705.07853},
author = {Kuzborskij, Ilja and Cesa-Bianchi, Nicol{\`{o}}},
booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, {\{}USA{\}}},
eprint = {1705.07853},

pages = {667--676},
title = {{Nonparametric Online Regression while Learning the Metric}},

year = {2017}
}
@article{Lee2016,
author = {Lee, Minwoo and Anderson, Charles W},

journal = {Proceedings of the 1st international workshop on robot learning and planning (RLP)},
number = {Rlp},
pages = {5--12},
title = {{Robust reinforcement learning with relevance vector machines}},
year = {2016}
}
@inproceedings{DBLP:conf/smc/Oza05,
abstract = {Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by describing some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time.},
archivePrefix = {arXiv},
arxivId = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.8889},
author = {Oza, N.C.},
booktitle = {Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, Hawaii, USA, October 10-12, 2005},

eprint = {/citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.8889},

isbn = {0-7803-9298-1},
issn = {1062922X},
keywords = {Bagging,boosting,ensemble learning,online learning},
pages = {2340--2345},
pmid = {25246403},
primaryClass = {http:},
publisher = {IEEE},
title = {{Online Bagging and Boosting}},

volume = {3},
year = {2006}
}
@article{Wang2006,
abstract = { Current models of the classification problem do not effectively handle bursts of particular classes coming in at different times. In fact, the current model of the classification problem simply concentrates on methods for one-pass classification modeling of very large data sets. Our model for data stream classification views the data stream classification problem from the point of view of a dynamic approach in which simultaneous training and test streams are used for dynamic classification of data sets. This model reflects real-life situations effectively, since it is desirable to classify test streams in real time over an evolving training and test stream. The aim here is to create a classification system in which the training model can adapt quickly to the changes of the underlying data stream. In order to achieve this goal, we propose an on-demand classification process which can dynamically select the appropriate window of past training data to build the classifier. The empirical results indicate that the system maintains an high classification accuracy in an evolving data stream, while providing an efficient solution to the classification task.},
author = {Wang, Jianyong},


isbn = {1041-4347 VO  - 18},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Geometric time frame,Microclustering,Nearest neighbor,Stream classification},
number = {5},
pages = {577--589},
title = {{A framework for on-demand classification of evolving data streams}},
volume = {18},
year = {2006}
}
@inproceedings{Augenstein.2017,
abstract = {The importance of processing stream data increases with new technologies and new use cases. Applying machine learning to stream data and process them in real time leads to challenges in different ways. Model changes, concept drift or insufficient time to train models are a few examples. We illustrate big data characteristics and machine learning techniques derived from literature and conclude with available approaches and drawbacks.},
author = {Augenstein, Christoph and Spangenberg, Norman and Franczyk, Bogdan},
booktitle = {IEEE 4th International Conference on Soft Computing and Machine Intelligence, ISCMI 2017},


isbn = {9781538613146},
keywords = {big data,challenges,data streams,machine learning},
month = {nov},
pages = {25--29},
title = {{Applying machine learning to big data streams : An overview of challenges}},
volume = {2018-Janua},
year = {2018}
}
@inproceedings{Zhu2017,
author = {Zhu, Lin and Zhang, Xiang and Zhang, Wenju and Huang, Xuhui and Guan, Naiyang and Luo, Zhigang},
booktitle = {2017 IEEE International Conference on Image Processing (ICIP)},


isbn = {978-1-5090-2175-8},
month = {sep},
number = {2015},
pages = {3066--3070},
publisher = {IEEE},
title = {{Unsupervised domain adaptation with joint supervised sparse coding and discriminative regularization term}},

year = {2017}
}
@article{Ioffe2015,
abstract = {Simulations of gas seeding into a hypersonic boundary layer flow were performed using OpenFOAM to investigate and quantify errors associated with quantitative planar laser induced fluorescence thermometry and velocimetry techniques. A modified version of the compressible rhoCentralFoam solver was used to simulate multicomponent chemically reactive flows. Simulations replicated conditions used in NASA Langley's 31” Mach 10 facility with a wedge model oriented at various angles of attack. The magnitude and location of potential reactions were estimated through the use of an analytical half-life model and non-reactive simulations. These estimates were compared to the reactive simulations. The order of magnitude predictions were found to be accurate when a simulated estimate of the distribution of nitric oxide was used. The effect of the heat release due to chemical reactions on the velocity and temperature profiles was investigated and found to be negligible for all positive angles of attack.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Arisman},

eprint = {arXiv:1011.1669v3},

isbn = {9780874216561},
issn = {0717-6163},
journal = {Thesis},
keywords = {Bott},
number = {1},
pages = {1--64},
pmid = {15003161},
title = {{Nitric Oxide Chemistry and Velocity Slip Effects in Hypersonic Boundary Layers}},

year = {2014}
}
@article{givens1984,
author = {Givens, Clark R and Shortt, Rae Michael},


journal = {The Michigan Mathematical Journal},
number = {2},
pages = {231--240},
publisher = {University of Michigan, Department of Mathematics},
title = {{A class of Wasserstein metrics for probability distributions.}},

volume = {31},
year = {1984}
}
@incollection{NIPS2017_6963,
author = {Courty, Nicolas and Flamary, R{\'{e}}mi and Habrard, Amaury and Rakotomamonjy, Alain},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {Guyon, I and Luxburg, U V and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R},

pages = {3730--3739},
publisher = {Curran Associates, Inc.},
title = {{Joint distribution optimal transportation for domain adaptation}},

year = {2017}
}
@article{Saralajew2018,
abstract = {Neural networks currently dominate the machine learning community and they do so for good reasons. Their accuracy on complex tasks such as image classification is unrivaled at the moment and with recent improvements they are reasonably easy to train. Nevertheless, neural networks are lacking robustness and interpretability. Prototype-based vector quantization methods on the other hand are known for being robust and interpretable. For this reason, we propose techniques and strategies to merge both approaches. This contribution will particularly highlight the similarities between them and outline how to construct a prototype-based classification layer for multilayer networks. Additionally, we provide an alternative, prototype-based, approach to the classical convolution operation. Numerical results are not part of this report, instead the focus lays on establishing a strong theoretical framework. By publishing our framework and the respective theoretical considerations and justifications before finalizing our numerical experiments we hope to jump-start the incorporation of prototype-based learning in neural networks and vice versa.},
archivePrefix = {arXiv},
arxivId = {1812.01214},
author = {Saralajew, Sascha and Holdijk, Lars and Rees, Maike and Villmann, Thomas},
eprint = {1812.01214},

journal = {arXiv},
pages = {1--19},
title = {{Prototype-based Neural Network Layers: Incorporating Vector Quantization}},

year = {2018}
}
@article{JudeNirmal2017,
author = {{Jude Nirmal}, V. and {George Amalarethinam}, D. I.},


isbn = {9781509055739},
journal = {Proceedings - 2nd World Congress on Computing and Communication Technologies, WCCCT 2017},
keywords = {Hadoop,Opinion Mining,Parallel Na{\"{i}}ve Bayes,Sentiment Analysis,Spark},
pages = {69--72},
title = {{Real-Time Sentiment Prediction on Streaming Social Network Data Using In-Memory Processing}},
year = {2017}
}
@inproceedings{Hofmann.2012,
abstract = {Prototype-based classification schemes offer very intuitive and flexible classifiers with the benefit of easy interpretability of the results and scalability of the model complexity. Recent prototype-based models such as robust soft learning vector quantization (RSLVQ) have the benefit of a solid mathematical foundation of the learning rule and decision boundaries in terms of probabilistic models and corresponding likelihood optimization. In its original form, they can be used for standard Euclidean vectors only. In this contribution, we extend RSLVQ towards a kernelized version which can be used for any positive semidefinite data matrix. We demonstrate the superior performance of the technique, kernel RSLVQ, in a variety of benchmarks where results competitive or even superior to state-of-the-art support vector machines are obtained.},
address = {Berlin, Heidelberg},
author = {Hofmann, Daniela and Hammer, Barbara},
booktitle = {Artificial Neural Networks in Pattern Recognition},
editor = {Mana, Nadia and Schwenker, Friedhelm and Trentin, Edmondo},

isbn = {978-3-642-33212-8},
pages = {14--23},
publisher = {Springer Berlin Heidelberg},
title = {{Kernel Robust Soft Learning Vector Quantization}},
year = {2012}
}
@article{Angelov.2008,
abstract = {A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (evolving) fuzzy-rule-based (FRB) classifier system of Takagi-Sugeno (eTS) type. The proposed approach, called eClass (eclassifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1) eClass0, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order eTS fuzzy classifier, eClass1. An important property of eClass is that it can start learning "from scratch." Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for eClass (the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists, eClass can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources. {\textcopyright} 2008 IEEE.},
author = {Angelov, Plamen P. and Zhou, Xiaowei},


issn = {10636706},
journal = {IEEE Transactions on Fuzzy Systems},
keywords = {Evolving fuzzy systems,Fuzzy-rule-based (FRB) classifiers,Recursive least squares (RLS),Takagi-Sugeno fuzzy models},
number = {6},
pages = {1462--1475},
title = {{Evolving fuzzy-rule-based classifiers from data streams}},

volume = {16},
year = {2008}
}
@article{Jhuo2012,
abstract = {Visual domain adaptation addresses the problem of adapting the sample distribution of the source domain to the target domain, where the recognition task is intended but the data distributions are different. In this paper, we present a low-rank reconstruction method to reduce the do- main distribution disparity. Specifically, we transform the visual samples in the source domain into an intermediate representation such that each transformed source sample can be linearly reconstructed by the samples of the target domain. Unlike the existing work, our method captures the intrinsic relatedness of the source samples during the adap- tation process while uncovering the noises and outliers in the source domain that cannot be adapted, making it more robust than previous methods. We formulate our problem as a constrained nuclear norm and 2,1 norm minimization ob- jective and then adopt the Augmented Lagrange Multiplier (ALM) method for the optimization. Extensive experiments on various visual adaptation tasks show that the proposed method consistently and significantly beats the state-of-the- art domain adaptation methods.},
author = {Jhuo, I. Hong and Liu, Dong and Lee, D. T. and Chang, Shih Fu},


isbn = {9781467312264},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {2168--2175},
publisher = {IEEE},
title = {{Robust visual domain adaptation with low-rank reconstruction}},
year = {2012}
}
@inproceedings{Cao2019,
abstract = {In the cross-domain image classification scenario, domain adaption aims to address the challenge of transferring the knowledge obtained from the source domain to the target domain that is regarded as similar but different from the source domain. To get more reliable domain invariant representations, recent methods start to consider class-level distribution alignment across the source and target domains by adaptively assigning pseudo target labels. However, these approaches are vulnerable to the error accumulation and hence unable to preserve cross-domain category consistency. Because the accuracy of pseudo labels cannot be guaranteed explicitly. In this paper, we propose Adversarial Domain Adaptation with Semantic Consistency (ADASC) model to align the discriminative features across domains progressively and effectively, via exploiting the class-level relations between domains. Specifically, to simultaneously alleviate the negative influence of the false pseudo-target labels and get the discriminative domain invariant features, we introduce an Adaptive Centroid Alignment (ACA) strategy and a Class Discriminative Constraint (CDC) step to complement each other iteratively and alternatively in an end-to-end framework. Extensive experiments are conducted on several unsupervised domain adaptation datasets, and the results show that ADASC outperforms the state-of-the-art methods.},
address = {New York, NY, USA},
author = {Cao, Manliang and Zhou, Xiangdong and Xu, Yiming and Pang, Yue and Yao, Bo},
booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},


isbn = {9781450369763},
keywords = {Adversarial Learning,Classification,Domain Adaptation,Semantic Consistency},
month = {nov},
pages = {259--268},
publisher = {ACM},
title = {{Adversarial Domain Adaptation with Semantic Consistency for Cross-Domain Image Classification}},

year = {2019}
}
@article{Redmon2018,
abstract = {We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at https://pjreddie.com/yolo/},
archivePrefix = {arXiv},
arxivId = {1804.02767},
author = {Redmon, Joseph and Farhadi, Ali},
eprint = {1804.02767},

month = {apr},
title = {{YOLOv3: An Incremental Improvement}},

year = {2018}
}
@article{Samangouei2018,
abstract = {Structure: see text D-fructose-6-phosphate aldolase (FSA) mediates a novel straightforward two-step chemo-enzymatic synthesis of D-fagomine and some of its N-alkylated derivatives in 51{\%} isolated yield and 99{\%} de. The key step is the FSA-catalyzed aldol addition of simple dihydroxyacetone (DHA) to N-Cbz-3-aminopropanal. The use of FSA greatly simplifies the enzymatic procedures that used dihydroxyacetonephosphate or DHA/esters. Some N-alkyl derivatives synthesized elicited antifungal and antibacterial activity as well as enhanced inhibitory activity, and selectivity against beta-galactosidase and alpha-glucosidase.},
archivePrefix = {arXiv},
arxivId = {1805.06605},
author = {Castillo, Jos{\'{e}} A. and Calveras, Jordi and Casas, Josefina and Mitjans, Montserrat and Vinardell, M. Pilar and Parella, Teodor and Inoue, Tomoyuki and Sprenger, Georg A. and Joglar, Jes{\'{u}}s and Clap{\'{e}}s, Pere},

eprint = {1805.06605},

isbn = {1523-7060 (Print) 1523-7052 (Linking)},
issn = {15237060},
journal = {Organic Letters},
number = {26},
pages = {6067--6070},
pmid = {17165931},
title = {{Fructose-6-phosphate aldolase in organic synthesis: Preparation of D-fagomine, N-alkylated derivatives, and preliminary biological assays}},

volume = {8},
year = {2006}
}
@article{Long2018,
abstract = {Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. How-ever, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representa-tions of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statisti-cal guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empiri-cal evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.},
archivePrefix = {arXiv},
arxivId = {1502.02791},
author = {Long, Mingsheng and Cao, Yue and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I.},

eprint = {1502.02791},

isbn = {9781510810587},
issn = {19393539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Adaptation models,Convolutional neural networks,Domain adaptation,Gallium nitride,Kernel,Task analysis,Testing,convolutional neural network,deep learning,multiple kernel learning,two-sample test},
number = {c},
pages = {1},
pmid = {23027094},
publisher = {IEEE},
title = {{Transferable Representation Learning with Deep Adaptation Networks}},
volume = {PP},
year = {2018}
}
@article{doi:10.1080/01621459.1937.10503522,
abstract = {AbstractDownload full textRelated articlesView all related articles$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n$\backslash$r$\backslash$n var addthis{\_}config = {\{}$\backslash$r$\backslash$n ui{\_}cobrand: "Taylor {\&} Francis Online",$\backslash$r$\backslash$n services{\_}compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more",$\backslash$r$\backslash$n pubid: "ra-4dff56cd6bb1830b"$\backslash$r$\backslash$n {\}};$\backslash$r$\backslash$n$\backslash$r$\backslash$n Share on facebook$\backslash$r$\backslash$n Share on twitter$\backslash$r$\backslash$n Share on email$\backslash$r$\backslash$n More Sharing Services$\backslash$r$\backslash$n $\backslash$r$\backslash$n var addthis{\_}config = {\{}"data{\_}track{\_}addressbar":true,"ui{\_}click":true{\}};$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n Add to shortlist$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n Link$\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n Permalink$\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n$\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n$\backslash$r$\backslash$n$\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n http://dx.doi.org/10.1080/01621459.1937.10503522$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n Download Citation$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n Recommend to:$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n $\backslash$r$\backslash$n$\backslash$r$\backslash$n A friend},
author = {Friedman, Milton},


issn = {1537274X},
journal = {Journal of the American Statistical Association},
number = {200},
pages = {675--701},
publisher = {Taylor {\&} Francis},
title = {{The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance}},

volume = {32},
year = {1937}
}
@article{Pennington2014a,
abstract = {Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arith- metic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global log- bilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word co- occurrence matrix, rather than on the en- tire sparse matrix or on individual context windows in a large corpus. On a recent word analogy task our model obtains 75{\%} accuracy, an improvement of 11{\%} over Mikolov et al. (2013). It also outperforms related word vector models on similarity tasks and named entity recognition.},
author = {Pennington, Jeffrey and Socher, Richard and Manning, Christopher},


journal = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
pages = {1532--1543},
title = {{Glove: Global Vectors for Word Representation}},

year = {2014}
}
@article{Chen2009,
abstract = {In this paper, a sparse learning algorithm, probabilistic classification vector machines (PCVMs), is proposed. We analyze relevance vector machines (RVMs) for classification problems and observe that adopting the same prior for different classes may lead to unstable solutions. In order to tackle this problem, a signed and truncated Gaussian prior is adopted over every weight in PCVMs, where the sign of prior is determined by the class label, i.e., +1 or -1. The truncated Gaussian prior not only restricts the sign of weights but also leads to a sparse estimation of weight vectors, and thus controls the complexity of the model. In PCVMs, the kernel parameters can be optimized simultaneously within the training algorithm. The performance of PCVMs is extensively evaluated on four synthetic data sets and 13 benchmark data sets using three performance metrics, error rate (ERR), area under the curve of receiver operating characteristic (AUC), and root mean squared error (RMSE). We compare PCVMs with soft-margin support vector machines (SVM(Soft)), hard-margin support vector machines (SVM(Hard)), SVM with the kernel parameters optimized by PCVMs (SVM(PCVM)), relevance vector machines (RVMs), and some other baseline classifiers. Through five replications of twofold cross-validation F test, i.e., 5 x 2 cross-validation F test, over single data sets and Friedman test with the corresponding post-hoc test to compare these algorithms over multiple data sets, we notice that PCVMs outperform other algorithms, including SVM(Soft), SVM(Hard), RVM, and SVM(PCVM), on most of the data sets under the three metrics, especially under AUC. Our results also reveal that the performance of SVM(PCVM) is slightly better than SVM(Soft), implying that the parameter optimization algorithm in PCVMs is better than cross validation in terms of performance and computational complexity. In this paper, we also discuss the superiority of PCVMs' formulation using maximum a posteriori (MAP) analysis and margin analysis, which explain the empirical success of PCVMs.},
archivePrefix = {arXiv},
arxivId = {1304.3345},
author = {Chen, Huanhuan and Tiňo, Peter and Yao, Xin},

eprint = {1304.3345},

issn = {10459227},
journal = {IEEE Transactions on Neural Networks},
keywords = {Bayesian classification,Machine learning,Probabilistic classification model,Support vector machine},
number = {6},
pages = {901--914},
pmid = {19398403},
title = {{Probabilistic classification vector machines}},
volume = {20},
year = {2009}
}
@inproceedings{Wang.2010,
abstract = {Abstract-A probabilistic data stream S is defined as a sequence of uncertain tuples {\textless};ti, pi {\textgreater};, i = 1...∞, with the semantics that element ti occurs in the stream with probability pi ϵ (0, 1). Thus each distinct element t, which occurs in tuples of S, has an existential probability based on the tuples: {\textless}; ti = t, pi {\textgreater}; ϵ S. Existing duplicate detection methods for a traditional deterministic data stream can't maintain these existential probabilities for elements in S, which is important query information. In this paper, we present a novel data structure, Floating Counter Bloom Filter (FCBF), as an extension of CBF, which can maintain these existential probabilities effectively. Based on FCBF, we present an efficient algorithm to approximately detect duplicates for probabilistic data streams over sliding windows. Given a sliding window size W and floating counter number N, for any t which occurs in the past sliding window, our method outputs the accurate existential probability of t with probability 1-(1/2)ln(2)*N/W. Our experimental results on the synthetic data verify the effectiveness of our approach.},
author = {Wang, Xiujun and Shen, Hong},
booktitle = {Proceedings - 3rd International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2010},


isbn = {9780769543123},
issn = {2168-3034},
keywords = {Counting bloom filter,Duplicate detection,False positive,Floating counter bloom filter,Probabilistic data stream},
pages = {263--268},
title = {{Approximately detecting duplicates for probabilistic data streams over sliding windows}},
year = {2010}
}
@inproceedings{Casillas2019,
author = {Casillas, Jorge and Wang, Shuo and Yao, Xin},
booktitle = {IEEE International Conference on Data Mining Workshops, ICDMW},


isbn = {9781538692882},
issn = {23759259},
month = {nov},
pages = {878--885},
publisher = {IEEE},
title = {{Concept drift detection in histogram-based straightforward data stream prediction}},

volume = {2018-Novem},
year = {2019}
}
@article{Ben-David2010,
abstract = {Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. Often, however, we have plentiful labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and little or no labeled training data. In this work we investigate two questions. First, under what conditions can a classifier trained from source data be expected to perform well on target data? Second, given a small amount of labeled target data, how should we combine it during training with the large amount of labeled source data to achieve the lowest target error at test time? We address the first question by bounding a classifier's target error in terms of its source error and the divergence between the two domains. We give a classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains. Under the assumption that there exists some hypothesis that performs well in both domains, we show that this quantity together with the empirical source error characterize the target error of a source-trained classifier. We answer the second question by bounding the target error of a model which minimizes a convex combination of the empirical source and target errors. Previous theoretical work has considered minimizing just the source error, just the target error, or weighting instances from the two domains equally. We show how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class. The resulting bound generalizes the previously studied cases and is always at least as tight as a bound which considers minimizing only the target error or an equal weighting of source and target errors. {\textcopyright} The Author(s) 2009.},
author = {Ben-David, Shai and Blitzer, John and Crammer, Koby and Kulesza, Alex and Pereira, Fernando and Vaughan, Jennifer Wortman},


issn = {0885-6125},
journal = {Machine Learning},
keywords = {Domain adaptation,Learning theory,Sample-selection bias,Transfer learning},
month = {may},
number = {1-2},
pages = {151--175},
title = {{A theory of learning from different domains}},

volume = {79},
year = {2010}
}
@article{Bottou2010,
abstract = {During the last decade, the data sizes have grown faster than the speed of processors. In this context, the capabilities of statistical machine learning methods is limited by the computing time rather than the sample size. A more precise analysis uncovers qualitatively different tradeoffs for the case of small-scale and large-scale learning problems. The large-scale case involves the computational complexity of the underlying optimization algorithm in non-trivial ways. Unlikely optimization algorithms such as stochastic gradient descent show amazing performance for large-scale problems. In particular, second order stochastic gradient and averaged stochastic gradient are asymptotically efficient after a single pass on the training set.},
author = {Bottou, L{\'{e}}on},


isbn = {9783790826036},
issn = {0269-2155},
journal = {Proceedings of COMPSTAT 2010 - 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers},
keywords = {Efficiency,Online learning,Stochastic gradient descent},
pages = {177--186},
pmid = {20876631},
title = {{Large-scale machine learning with stochastic gradient descent}},
year = {2010}
}
@article{Chen2018a,
abstract = {The success of deep neural networks in computer vision tasks requires a large number of annotated samples which are not available for many applications. In the absence of annotated data, domain adaptation provides an avenue to train deep neural networks effectively by utilizing the labeled data from a different but similar domain. In this paper, we propose a new Deep Domain Similarity Adaptation Network (DDSAN) architecture, which can exploit the labeled data from the source domain and unlabeled data from the target domain simultaneously. The DDSAN assumes that the parameters of the deep networks from source and target domains should be close to each other. Then, we transfer the deep network parameters from different domains explicitly instead of matching the deep hidden representations implicitly. By plugging a subnet into the typical deep neural networks, the DDSAN can project the high-dimensional parameters to a lower dimensional subspace and reduce their domain discrepancies. Comparative experiments demonstrate that the proposed network outperforms previous methods on the standard domain adaptation benchmarks.},
author = {Chen, Yu and Yang, Chunling and Zhang, Yan},


issn = {01678655},
journal = {Pattern Recognition Letters},
keywords = {Deep learning,Domain adaptation,Domain similarity,Image classification},
month = {sep},
pages = {270--276},
publisher = {Elsevier B.V.},
title = {{Deep domain similarity Adaptation Networks for across domain classification}},

volume = {112},
year = {2018}
}
@article{Shao2014a,
abstract = {It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods. {\textcopyright} 2014 Springer Science+Business Media New York.},
annote = {Solve of Optimization Problem as aspected.
However, low rank transformation is seeked. Outlier which may affect the global low rank approximation are eliminated by and error term. Subspace Transfromation is learned via augmented Lagrange Multiplier.

1T Z = 1T denotes ||Z ||=1

Segmentation of related work:
Transductive/Inductive
Reweighting/Subspace
Application (Visual)
Discussion

Opt Problem:
- This formulation prevents effective knowledge transfer,
because it violates the observation that data spans multi- ple subspaces.

- In the previous section, we avoided trivial solutions of subspace guarantees that each target datum is appropriately Xt = XsZ by using low-rank constraint, and the recovery of reconstructed by some neighborhood of the source data.

Low-Rank Discussion:
- First thanks to the low-rank constraint, our method provides accurate alignment for source and target domains, and is robust in the presence of noise, gross corruptions of data and outliers.
- By placing a low-rank constraint on the coeffi- cient matrix Z, we can achieve a better representation of the target data, and therefore Problem 1 can be reformulated},
author = {Shao, Ming and Kit, Dmitry and Fu, Yun},


issn = {15731405},
journal = {International Journal of Computer Vision},
keywords = {Domain adaptation,Low-rank constraint,Subspace learning,Transfer learning},
number = {1-2},
pages = {74--93},
title = {{Generalized transfer subspace learning through low-rank constraint}},
volume = {109},
year = {2014}
}
@incollection{Jiang2012,
abstract = {Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its adoption in clustering however is still limited. Despite of its superior performance, spectral clustering has not yet been incorporated with knowledge transfer or transfer learning. In this paper, we make such an attempt and propose a new algorithm called transfer spectral clustering (TSC). It involves not only the data manifold information of the clustering task but also the feature manifold information shared between related clustering tasks. Furthermore, it makes use of co-clustering to achieve and control the knowledge transfer among tasks. As demonstrated by the experimental results, TSC can greatly improve the clustering performance by effectively using auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms. {\textcopyright} 2012 Springer-Verlag.},
author = {Jiang, Wenhao and Chung, Fu-lai},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},


isbn = {9783642334856},
issn = {03029743},
keywords = {Co-clustering,Spectral Clustering,Transfer Learning},
number = {PART 2},
pages = {789--803},
title = {{Transfer Spectral Clustering}},

volume = {7524 LNAI},
year = {2012}
}
@article{Dong2017,
abstract = {Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of existing adversarial attacks can only fool a black-box model with a low success rate. To address this issue, we propose a broad class of momentum-based iterative algorithms to boost adversarial attacks. By integrating the momentum term into the iterative process for attacks, our methods can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples. To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks. We hope that the proposed methods will serve as a benchmark for evaluating the robustness of various deep models and defense methods. With this method, we won the first places in NIPS 2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.},
archivePrefix = {arXiv},
arxivId = {1710.06081},
author = {Dong, Yinpeng and Liao, Fangzhou and Pang, Tianyu and Su, Hang and Zhu, Jun and Hu, Xiaolin and Li, Jianguo},

eprint = {1710.06081},

isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {9185--9193},
title = {{Boosting Adversarial Attacks with Momentum}},

year = {2018}
}
@article{Lillicrap2015,
abstract = {Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g., in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.},
archivePrefix = {arXiv},
arxivId = {1509.02971},
author = {Bengio, Y.},

eprint = {1509.02971},

isbn = {2200000006},
issn = {1935-8237},
journal = {Foundations and Trends{\textregistered} in Machine Learning},
number = {1},
pages = {1--127},
pmid = {24966830},
title = {{Learning Deep Architectures for AI}},

volume = {2},
year = {2009}
}
@article{DBLP:journals/ijon/SchleifGT18,
author = {Schleif, Frank-Michael and Gisbrecht, Andrej and Ti{\~{n}}o, Peter},

journal = {Neurocomputing},
pages = {213--226},
title = {{Supervised low rank indefinite kernel approximation using minimum enclosing balls}},

volume = {318},
year = {2018}
}
@article{DBLP:journals/tnn/ZhangK10a,
abstract = {Kernel (or similarity) matrix plays a key role in many machine learning algorithms such as kernel methods, manifold learning, and dimension reduction. However, the cost of storing and manipulating the complete kernel matrix makes it infeasible for large problems. The Nystr{\"{o}}m method is a popular sampling-based low-rank approximation scheme for reducing the computational burdens in handling large kernel matrices. In this paper, we analyze how the approximating quality of the Nystr{\"{o}}m method depends on the choice of landmark points, and in particular the encoding powers of the landmark points in summarizing the data. Our (non-probabilistic) error analysis justifies a "clustered Nystr{\"{o}}m method" that uses the k-means clustering centers as landmark points. Our algorithm can be applied to scale up a wide variety of algorithms that depend on the eigenvalue decomposition of kernel matrix (or its variant), such as kernel principal component analysis, Laplacian eigenmap, spectral clustering, as well as those involving kernel matrix inverse such as least-squares support vector machine and Gaussian process regression. Extensive experiments demonstrate the competitive performance of our algorithm in both accuracy and efficiency.},
author = {Zhang, Kai and Kwok, James T.},

issn = {10459227},
journal = {IEEE Transactions on Neural Networks},
keywords = {Dimension reduction,Nystrm method,eigenvalue decomposition,kernel matrix,low-rank approximation,manifold learning,sampling},
number = {10},
pages = {1576--1587},
title = {{Clustered Nystr{\"{o}}m method for large scale manifold learning and dimension reduction}},
volume = {21},
year = {2010}
}
@article{Lampert2009,
abstract = {必参考文献，重复其工作},
author = {Lampert, Christoph H and Harmeling, Stefan},

isbn = {9781424439911},
journal = {Cvpr},
pages = {951--958},
title = {{Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer}},
year = {2009}
}
@article{Jordaney2017,
abstract = {Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen behaviors. Consequently, most malware classifiers become unsustainable in the long run, becoming rapidly antiquated as malware continues to evolve. In this work, we propose Transcend, a framework to identify aging classification models in vivo during deployment, much before the machine learning model's performance starts to degrade. This is a significant departure from conventional approaches that retrain aging models retrospectively when poor performance is observed. Our approach uses a statistical comparison of samples seen during deployment with those used to train the model, thereby building metrics for prediction quality. We show how Transcend can be used to identify concept drift based on two separate case studies on Android andWindows malware, raising a red flag before the model starts making consistently poor decisions due to out-of-date training.},
author = {Jordaney, Roberto and Holloway, Royal and Sharad, Kumar and Laboratories, N E C and Dash, Santanu K and Wang, Zhi and Papini, Davide and Elettronica, S A and Nouretdinov, Ilia and Cavallaro, Lorenzo},

isbn = {9781931971409},
journal = {USENIX Security},
pages = {625--642},
title = {{Transcend : Detecting Concept Drift in Malware Classification Models This paper is included in the Proceedings of the Transcend : Detecting Concept Drift in Malware Classification Models}},

year = {2017}
}
@inproceedings{10.1007/978-3-030-24265-7_9,
abstract = {Domain adaption aims to promote the learning tasks in target domain by using the knowledge from source domain whose data distribution is different from target domain. How to reconstruct more robust and high-level feature space to reduce the discrepancy between source and target domains is the crucial problem in domain adaptation. Recently, deep learning methods have shown promising results on learning new representations. However, in most of the previous work, the local geometry structure of data is not taken into account, which is benefit for constructing more robust feature space. Therefore, we propose a novel algorithm termed {\{}$\backslash${\$}{\}}{\{}\backslash{\$}{\}}{\{}$\backslash$backslash{\}}ell {\{}$\backslash${\_}{\}}{\{}$\backslash${\{}{\}}2,1{\{}$\backslash${\}}{\}}{\{}$\backslash${\$}{\}}{\{}\backslash{\$}{\}}-norm stacked robust autoencoders via adaptation regularization (SRAAR) to learn better feature representations for domain adaptation. More specifically, we incorporate an effective manifold regularization into the objective function to preserve the local geometry structure of data. In addition, label information is used to compute the maximum mean discrepancy (MMD) in order to reduce the distance between source and target domains. Experimental results show the effectiveness of proposed approach.},
address = {Cham},
author = {Yang, Shuai and Zhang, Yuhong and Zhu, Yi and Ding, Han and Hu, Xuegang},
booktitle = {Artificial Intelligence and Security},
editor = {Sun, Xingming and Pan, Zhaoqing and Bertino, Elisa},

isbn = {978-3-030-24265-7},
pages = {100--111},
publisher = {Springer International Publishing},
title = {{The {\{}$\backslash${\$}{\}}{\{}\backslash{\$}{\}}{\{}$\backslash$backslash{\}}ell {\{}$\backslash${\_}{\}}{\{}$\backslash${\{}{\}}2,1{\{}$\backslash${\}}{\}}{\{}$\backslash${\$}{\}}{\{}\backslash{\$}{\}}-Norm Stacked Robust Autoencoders via Adaptation Regularization for Domain Adaptation}},
year = {2019}
}
@article{Bharitkar2001,
author = {Bharitkar, S and Filev, D},

journal = {Signal Processing and its Applications, {\ldots}},
pages = {394--397},
title = {{An online learning vector quantization algorithm}},

year = {2001}
}
@inproceedings{miyato2018spectral,
annote = {GAN Regularization via Spectral Normalization of WEIGHTS.

Training of GAN is unstalbe and ineffective. Meaning the discriminator the high density ratio estimation is inaccurat and therefore the generator fails to learn (Exploding Gradient). Further if the discriminator perfectly classifies generated and real images, the gradient becomes zero.

To control the gradient of the GAN the authors proposed spectral normalization on the (discriminator) network by K-Lipschitz..
K-Lipschitz == sigma{\_},max(W), where W is the linear operator and sigma{\_}max(.) is the largest singular value of M. therfore for begin 1-Lipschitz it is sufficient to restrict sigm{\_}max(W) =1.

Author proves that this also applies to compositve functions (neural network)

Gradient of normalized W with resepct to W is done to show what happens to weights after normalization: The derivative shows that the gradient and produced output are positive if point in the same direction. Therefore are not to sensitive in one direction because it will point in other direction if not similar to not to be specific in a particular dimension.

In contrast to others regularizations it does not rectrict the weights to be a certain small rank (losing information).},
author = {Miyato, Takeru and Kataoka, Toshiki and Koyama, Masanori and Yoshida, Yuichi},
booktitle = {International Conference on Learning Representations},

title = {{Spectral Normalization for Generative Adversarial Networks}},

year = {2018}
}
@incollection{NIPS2019_8470,
annote = {Lots of evaluated methods

Paper offers a nerw Batch normalization technique for synchronised domain adaptation networks.
BN Channelwise after activation data.

BN:
Advantages: Accelerate and stabilize training by controlling first and second moments of inputs
Input will be transformed to have zero varianze and rescale it to have identiy transformation
Importance of being able to represent the identity transform is that it ensures that you have not reduced the representational power of the network.


Transnorm:
1. Calculate Shareable Channelse via distance to normalization transformation per channel
2. Create a proability based on the distance and students-t distribution
3 .Applying domain wise of Batch normalization.
4.. Rescaling with shared gamma and beta.
5. Add aplha (2) to weight importance of channels to normalized outputs.

Integration: Replace all BN with TransNorm

Experiments:
CDAN+Transnorm
ResNet-50 +Transnorm
Office 31, ImageClef, Office-Home

Ablation Study
Different Probability types (Estimating alpa)
Different types of distances between normalization transformation.

Theoretical Study: Bound as in Ben-David and DANN:
Symmetric Hypothesis distance = A-Distance = 2(1-2e) where e is classifier trained to discriminate source domain and target domain

Convergence
Feature Visualization with Bottleneck features
Channel Visualization (Image)},
author = {Wang, Ximei and Jin, Ying and Long, Mingsheng and Wang, Jianmin and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d$\backslash$textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},

pages = {1951--1961},
publisher = {Curran Associates, Inc.},
title = {{Transferable Normalization: Towards Improving Transferability of Deep Neural Networks}},

year = {2019}
}
@article{Li2014,
abstract = {In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then develop two new feature mapping functions for two domains, which respectively augments the transformed source and target samples with their original features and padding zeros. Existing supervised learning methods (e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods.},
author = {Li, Wen and Duan, Lixin and Xu, Dong and Tsang, Ivor W.},


issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Heterogeneous domain adaptation,augmented features,domain adaptation,transfer learning},
number = {6},
pages = {1134--1148},
publisher = {IEEE},
title = {{Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation}},
volume = {36},
year = {2014}
}
@inproceedings{Hulten.2001,
abstract = {Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.},
author = {Leubuscher, G.},
booktitle = {Journal of Nervous and Mental Disease},


issn = {1539736X},
number = {1},
pages = {54},
title = {{Beitr{\"{a}}ge zur kenntniss und der behandlung der epilepsie (Contribution to the knowledge and treatment of epilepsy)}},

volume = {28},
year = {1901}
}
@article{Bach2016,
abstract = {A common approach for automatic sentiment classification is using classifiers trained on labeled text data (reviews, blog posts etc.) to predict the sentiment polarity of new data. Because people express sentiment differently in different domains, this approach requires annotated corpora for each domain. However, annotating data for every domain of interest is laborious and impractical. In this paper, we address the domain adaptation problem for sentiment classification. We explore the effect of generic methods for feature learning and feature subspace mapping, namely word embeddings and canonical correlation analysis (CCA), on cross-domain sentiment classifiers. We show that by using only such rather generic methods, it is possible to get results very competitive with those of sophisticated methods specially developed for the considered problem. An advantage of using word embeddings and CCA is their availability out-of-the-box, which is important for the applicability of the proposed method. Experiments on a widely used benchmark dataset shows that both word embeddings and CCA contribute to accuracy improvement and their combination provides the best results.},
author = {Bach, Ngo Xuan and Hai, Vu Thanh and Phuong, Tu Minh},

isbn = {9781450348157},
journal = {ACM International Conference Proceeding Series},
keywords = {Canonical correlation analysis,Cross-domain sentiment classification,Word embeddings},
pages = {159--166},
title = {{Cross-domain sentiment classification with word embeddings and canonical correlation analysis}},
volume = {08-09-Dece},
year = {2016}
}
@article{Tang2015,
abstract = {In this paper, we develop a deep learn-ing system for message-level Twitter sen-timent classification. Among the 45 sub-mitted systems including the SemEval 2013 participants, our system (Coooolll) is ranked 2nd on the Twitter2014 test set of SemEval 2014 Task 9. Coooolll is built in a supervised learning framework by concatenating the sentiment-specific word embedding (SSWE) features with the state-of-the-art hand-crafted features. We develop a neural network with hybrid loss function 1 to learn SSWE, which en-codes the sentiment information of tweets in the continuous representation of words. To obtain large-scale training corpora, we train SSWE from 10M tweets collected by positive and negative emoticons, without any manual annotation. Our system can be easily re-implemented with the publicly available sentiment-specific word embed-ding.},
author = {Tang, Duyu and Wei, Furu and Qin, Bing and Liu, Ting and Zhou, Ming},

number = {SemEval},
pages = {208--212},
title = {{Coooolll: A Deep Learning System for Twitter Sentiment Classification}},
year = {2015}
}
@article{Glorot2011a,
abstract = {The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains. Copyright 2011 by the author(s)/owner(s).},
author = {Glorot, Xavier and Bordes, Antoine and Bengio, Yoshua},
isbn = {9781450306195},
journal = {Proceedings of the 28th International Conference on Machine Learning, ICML 2011},
number = {1},
pages = {513--520},
title = {{Domain adaptation for large-scale sentiment classification: A deep learning approach}},
year = {2011}
}
@article{Bollegala2016,
abstract = {Unsupervised Cross-domain Sentiment Classification is the task of adapting a sentiment classifier trained on a particular domain (source domain), to a different domain (target domain), without requiring any labeled data for the target domain. By adapting an existing sentiment classifier to previously unseen target domains, we can avoid the cost for manual data annotation for the target domain. We model this problem as embedding learning, and construct three objective functions that capture: (a) distributional properties of pivots (i.e., common features that appear in both source and target domains), (b) label constraints in the source domain documents, and (c) geometric properties in the unlabeled documents in both source and target domains. Unlike prior proposals that first learn a lower-dimensional embedding independent of the source domain sentiment labels, and next a sentiment classifier in this embedding, our joint optimisation method learns embeddings that are sensitive to sentiment classification. Experimental results on a benchmark dataset show that by jointly optimising the three objectives we can obtain better performances in comparison to optimising each objective function separately, thereby demonstrating the importance of task-specific embedding learning for cross-domain sentiment classification. Among the individual objective functions, the best performance is obtained by (c). Moreover, the proposed method reports cross-domain sentiment classification accuracies that are statistically comparable to the current state-of-the-art embedding learning methods for cross-domain sentiment classification.},
author = {Bollegala, Danushka and Mu, Tingting and Goulermas, John Yannis},

issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Domain Adaptation,Embedding Learning,Sentiment Classification,Spectral Methods},
number = {2},
pages = {398--410},
publisher = {IEEE},
title = {{Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings}},
volume = {28},
year = {2016}
}
@article{Tang2015a,
abstract = {In this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-level sentiment analysis aims to automatically classify its sentiment/opinion (such as thumbs up or thumbs down ) based on the textural in-formation. Despite the success of feature engineering in many previous studies, the hand-coded features do not well capture the semantics of texts. In this research, we argue that learning sentiment-specific semantic representations of documents is crucial for document-level sentiment analysis. We decompose the document semantics into four cascad-ed constitutes: (1) word representation, (2) sentence struc-ture, (3) sentence composition and (4) document compo-sition. Specifically, we learn sentiment-specific word rep-resentations, which simultaneously encode the contexts of words and the sentiment supervisions of texts into the con-tinuous representation space. According to the principle of compositionality, we learn sentiment-specific sentence struc-tures and sentence-level composition functions to produce the representation of each sentence based on the representa-tions of the words it contains. The semantic representations of documents are obtained through document composition, which leverages the sentiment-sensitive discourse relations and sentence representations.},
author = {Tang, Duyu},

isbn = {9781450333177},
journal = {WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining},
keywords = {Deep learning,Natural language processing,Sentiment analysis},
pages = {447--451},
title = {{Sentiment-specific representation learning for document-level sentiment analysis}},
year = {2015}
}
@article{Sarma2018,
abstract = {Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a method to combine the breadth of generic embeddings with the specificity of domain specific embeddings. The resulting embeddings, called Domain Adapted (DA) word embeddings, are formed by aligning corresponding word vectors using Canonical Correlation Analysis (CCA) or the related nonlinear Kernel CCA. Evaluation results on sentiment classification tasks show that the DA embeddings substantially outperform both generic and DS embeddings when used as input features to standard or state-of-the-art sentence encoding algorithms for classification.},
archivePrefix = {arXiv},
arxivId = {1805.04576},
author = {Sarma, Prathusha K. and Liang, Yingyu and Sethares, William A.},

eprint = {1805.04576},
isbn = {9781948087346},
journal = {ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)},
pages = {37--42},
title = {{Domain adapted word embeddings for improved sentiment classification}},
volume = {2},
year = {2018}
}
@article{Huang2017a,
abstract = {Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-Term memory recurrent neural network (LSTM) is proposed. The model uses the pre-Trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).},
author = {Huang, Qiongxia and Chen, Riqing and Zheng, Xianghan and Dong, Zhenxing},

isbn = {9781538622803},
journal = {Proceedings - 2017 International Conference on Green Informatics, ICGI 2017},
keywords = {CNN,LSTM,Sentiment classification,Sentimentrepresentation},
pages = {30--33},
title = {{Deep sentiment representation based on CNN and LSTM}},
year = {2017}
}
@article{Tang2014,
abstract = {We present a method that learns word em- bedding for Twitter sentiment classifica- tion in this paper. Most existing algorithm- s for learning continuous word represen- tations typically only model the syntactic context of words but ignore the sentimen- t of text. This is problematic for senti- ment analysis as they usually map word- s with similar syntactic context but oppo- site sentiment polarity, such as good and bad, to neighboring word vectors. We address this issue by learning sentiment- specific word embedding (SSWE), which encodes sentiment information in the con- tinuous representation of words. Specif- ically, we develop three neural networks to effectively incorporate the supervision from sentiment polarity of text (e.g. sen- tences or tweets) in their loss function- s. To obtain large scale training corpora, we learn the sentiment-specific word em- bedding from massive distant-supervised tweets collected by positive and negative emoticons. Experiments on applying SS- WE to a benchmark Twitter sentimen- t classification dataset in SemEval 2013 show that (1) the SSWE feature performs comparably with hand-crafted features in the top-performed system; (2) the perfor- mance is further improved by concatenat- ing SSWE with existing feature set.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Tang, Duyu and Wei, Furu and Yang, Nan and Zhou, Ming and Liu, Ting and Qin, Bing},

eprint = {arXiv:1011.1669v3},
isbn = {9781937284725},
issn = {03029743},
journal = {Acl},
pages = {1555--1565},
pmid = {18487783},
title = {{Learning Sentiment-Specific Word Embedding}},
year = {2014}
}
@article{Yin2015,
abstract = {We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts features of multigranular phrases with variable-size convolution filters. We also show that pretraining MVCNN is critical for good performance. MVCNN achieves state-of-the-art performance on four tasks: on small-scale binary, small-scale multi-class and large-scale Twitter sentiment prediction and on subjectivity classification.},
archivePrefix = {arXiv},
arxivId = {1603.04513},
author = {Yin, Wenpeng and Sch{\"{u}}tze, Hinrich},

eprint = {1603.04513},
isbn = {9781941643778},
journal = {CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings},
pages = {204--214},
title = {{Multichannel variable-size convolution for sentence classification}},
year = {2015}
}
@article{Manshu2019a,
abstract = {Domain adaptation tasks have raised much attention in recent years, especially, the task of cross-domain sentiment classification (CDSC). Due to the domain discrepancy, a sentiment classifier trained in a source domain often performs less well, when directly applied to a target domain. Adversarial neural networks have been used in mainstream approaches for learning domain independent features, such as pivots, which are words with the same sentiment polarity in different domains. However, domain specific features can often determine sentiment in its context. In this paper, we propose a hierarchical attention network with prior knowledge information (HANP) for the CDSC task. Unlike other existing methods, the HANP can obtain both domain independent and domain specific features at the same time by adding prior knowledge. In addition, the HANP also includes a hierarchical representation layer with attention mechanism, so that the HANP can capture important words and sentences in relation to sentiment. Moreover, the proposed model can offer a direct visualization of the sentimental prior knowledge. The experiments on the Amazon review datasets demonstrate that the proposed HANP can significantly outperform the state-of-the-art methods.},
author = {Manshu, Tu and Bing, Wang},

issn = {21693536},
journal = {IEEE Access},
keywords = {Cross domain sentiment classification,HANP,prior knowledge},
pages = {32578--32588},
publisher = {IEEE},
title = {{Adding Prior Knowledge in Hierarchical Attention Neural Network for Cross Domain Sentiment Classification}},
volume = {7},
year = {2019}
}
@article{Nii2017,
abstract = {In this paper, we propose a convolutional neural network (CNN) based classification method for nursing-care classification. CNNs have obtained strong performance in computer vision speech recognition areas. Recently, CNNs have been also applied sentence classification. We have studied nursing-care text classification [6]-[18]. In our former works, we proposed several types of feature definitions and examined some classification models. In this paper, each text is represented as a concatenated word vector. Then, every text is classified using CNN-based classification methods. We examined some classification models at the classification layer in CNNs. From our experimental results, the proposed CNN-based method obtained better performance than our former works.},
author = {Nii, Manabu and Tsuchida, Yuya and Kato, Yusuke and Uchinuno, Atsuko and Sakashita, Reiko},

isbn = {9781509049172},
journal = {IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems},
keywords = {Nursing-care text classification,convolutional neural networks,word vector representation},
title = {{Nursing-care text classification using word vector representation and convolutional neural networks}},
year = {2017}
}
@article{Pan2011a,
abstract = {Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification. {\textcopyright} 2010 IEEE.},
author = {Pan, Sinno Jialin and Tsang, Ivor W. and Kwok, James T. and Yang, Qiang},

issn = {10459227},
journal = {IEEE Transactions on Neural Networks},
keywords = {Dimensionality reduction,Hilbert space embedding of distributions,domain adaptation,transfer learning},
number = {2},
pages = {199--210},
pmid = {21095864},
publisher = {IEEE},
title = {{Domain adaptation via transfer component analysis}},
volume = {22},
year = {2011}
}
@article{Araque2017,
abstract = {Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.},
author = {Araque, Oscar and Corcuera-Platas, Ignacio and S{\'{a}}nchez-Rada, J. Fernando and Iglesias, Carlos A.},

issn = {09574174},
journal = {Expert Systems with Applications},
keywords = {Deep learning,Ensemble,Machine learning,Natural language processing,Sentiment analysis},
pages = {236--246},
publisher = {Elsevier Ltd},
title = {{Enhancing deep learning sentiment analysis with ensemble techniques in social applications}},
volume = {77},
year = {2017}
}
@article{Zhang2016b,
abstract = {We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) - that capitalizes on multiple sets of word embeddings for sentence classification. MGNC-CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models.},
archivePrefix = {arXiv},
arxivId = {1603.00968},
author = {Zhang, Ye and Roller, Stephen and Wallace, Byron C.},

eprint = {1603.00968},
isbn = {9781941643914},
journal = {2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference},
pages = {1522--1527},
title = {{MGNC-CNN: A simple approach to exploiting multiple word embeddings for sentence classification}},
year = {2016}
}
@article{Manshu2019,
abstract = {Cross domain sentiment classification (CDSC) aims to adopt a model trained by a source domain to a target domain. It has received considerable attention in recent years. Most existing models mainly focus on learning representations that are domain independent in both the source domain and the target domain. However, domain specific features, which should also be informative are ignored by these models. In this paper, we propose an end to end model. It can capture both the source domain and target domain features at the same time. This model includes two parts; one is a cloze task network (CTN), we use it as an auxiliary task to fine-tune words embedding in both domains. Another is a Convolutional hierarchical attention networks (CHAN), we use it for sentiment classification. The CHAN can capture important words and sentences concerning sentiment based on its two stages of attention mechanism. The CTN and CHAN conduct jointly learning we abbreviate this model as CCHAN. The experiments on the Amazon review datasets demonstrate that the proposed CCHAN can significantly outperform the state-of-the-art methods.},
author = {Manshu, Tu and Xuemin, Zhao},

issn = {21693536},
journal = {IEEE Access},
keywords = {CCHAN,CDSC,end to end model},
pages = {50232--50239},
publisher = {IEEE},
title = {{CCHAN: An End to End Model for Cross Domain Sentiment Classification}},
volume = {7},
year = {2019}
}
@article{Xu2016,
abstract = {Recently, distributed word embeddings trained by neural language models are commonly used for text classification with Convolutional Neural Networks (CNNs). In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for biomedical literature indexing with CNNs. Topic-based Skip-gram leverages textual content with topic models, e.g., Latent Dirichlet Allocation (LDA), to capture precise topic-based word relationship and then integrate it into distributed word embedding learning. We then describe two multimodal CNN architectures, which are able to employ different kinds of word embeddings at the same time for text classification. Through extensive experiments conducted on several realworld datasets, we demonstrate that combination of our Topic-based Skip-gram and multimodal CNN architectures outperforms state-of-the-art methods in biomedical literature indexing, clinical note annotation and general textual benchmark dataset classification.},
author = {Xu, Haotian and Kotov, Alexander and Dong, Ming and Carcone, April Idalski and Zhu, Dongxiao and Naar-King, Sylvie},

isbn = {9781450342254},
journal = {ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics},
keywords = {Convolutional Neural Networks,Medical subject headings,Text classification,Word embeddings},
pages = {88--97},
title = {{Text classification with topic-based word embedding and Convolutional Neural Networks}},
year = {2016}
}
@book{Kamath,
author = {Kamath, Uday and Liu, John and Whitaker, James},
isbn = {9783030145958},
title = {{Deep Learning for NLP and Speech}}
}
@book{Deng2018,
abstract = {In the first part of this epilogue, we summarize the book holistically from two perspectives. The first, task-centric perspective ties together and categories a wide range of NLP techniques discussed in book in terms of general machine learning paradigms. In this way, the majority of sections and chapters of the book can be naturally clustered into four classes: classification, sequence-based prediction, higher-order structured prediction, and sequential decision-making. The second, representation-centric perspective distills insight from holistically analyzed book chapters from cognitive science viewpoints and in terms of two basic types of natural language representations: symbolic and distributed representations. In the second part of the epilogue, we update the most recent progress on deep learning in NLP (mainly during the later part of 2017, not surveyed in earlier chapters). Based on our reviews of these rapid recent advances, we then enrich our earlier writing on the research frontiers of NLP in Chap. 1 by addressing future directions of exploiting compositionality of natural language for generalization, unsupervised and reinforcement learning for NLP and their intricate connections, meta-learning for NLP, and weak-sense and strong-sense interpretability for NLP systems based on deep learning.},
author = {Deng, Li and Liu, Yang},
booktitle = {Deep Learning in Natural Language Processing},

isbn = {9789811052095},
pages = {309--326},
title = {{Epilogue: Frontiers of NLP in the deep learning era}},
year = {2018}
}
@article{Tang2016,
abstract = {We propose learning sentiment-specific word embeddings dubbed sentiment embeddings in this paper. Existing word embedding learning algorithms typically only use the contexts of words but ignore the sentiment of texts. It is problematic for sentiment analysis because the words with similar contexts but opposite sentiment polarity, such as good and bad, are mapped to neighboring word vectors. We address this issue by encoding sentiment information of texts (e.g., sentences and words) together with contexts of words in sentiment embeddings. By combining context and sentiment level evidences, the nearest neighbors in sentiment embedding space are semantically similar and it favors words with the same sentiment polarity. In order to learn sentiment embeddings effectively, we develop a number of neural networks with tailoring loss functions, and collect massive texts automatically with sentiment signals like emoticons as the training data. Sentiment embeddings can be naturally used as word features for a variety of sentiment analysis tasks without feature engineering. We apply sentiment embeddings to word-level sentiment analysis, sentence level sentiment classification, and building sentiment lexicons. Experimental results show that sentiment embeddings consistently outperform context-based embeddings on several benchmark datasets of these tasks. This work provides insights on the design of neural networks for learning task-specific word embeddings in other natural language processing tasks.},
author = {Tang, Duyu and Wei, Furu and Qin, Bing and Yang, Nan and Liu, Ting and Zhou, Ming},

issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Natural language processing,neural networks,sentiment analysis,word embeddings},
number = {2},
pages = {496--509},
publisher = {IEEE},
title = {{Sentiment Embeddings with Applications to Sentiment Analysis}},
volume = {28},
year = {2016}
}
@article{Liao2017,
abstract = {In this paper, we propose an approach to understand situations in the real world with the sentiment analysis of Twitter data base on deep learning techniques. With the proposed method, it is possible to predict user satisfaction of a product, happiness with some particular environment or destroy situation after disasters. Recently, deep learning is able to solve problems in computer vision or voice recognition, and convolutional neural network (CNN) works good for image analysis and image classification. The biggest reason to adopt CNN in image analysis and classification is due to CNN can extract an area of features from global information, and it is able to consider the relationship among these features. The above solution can achieve a higher accuracy in analysis and classification. For natural language processing, texts data features also can be extracted piece by piece and to consider the relationship among these features, but without the consideration of context or whole sentence, the sentiment might be understood wrong. And currently, convolutional neural network is one of the most effective methods to do image classification, CNN has a convolutional layer to extract information by a larger piece of text, so we work for sentiment analysis with convolutional neural network, and we design a simple convolutional neural network model and test it on benchmark, the result shows that it achieves better accuracy performance in twitter sentiment classification than some of traditional method such as the SVM and Naive Bayes methods.},
author = {Liao, Shiyang and Wang, Junbo and Yu, Ruiyun and Sato, Koichi and Cheng, Zixue},

isbn = {8124237271},
issn = {18770509},
journal = {Procedia Computer Science},
keywords = {convolutional neural network,data mining,deep learning,machine learning,sentiment analysis},
number = {2015},
pages = {376--381},
publisher = {Elsevier B.V.},
title = {{CNN for situations understanding based on sentiment analysis of twitter data}},

volume = {111},
year = {2017}
}
