Dataset Shift in Machine Learning
Language: English
Published by MIT Press, 2022
- Softcover
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Leichte Kratzer / Abnutzungen / Druckstellen. This volume provides an overview of recent efforts in the machine learning community to address dataset and covariate shift, which occur when training and test inputs and outputs have differing distributions. Dataset shift is a prevalent issue in predictive modeling, arising when the joint distribution of inputs and outputs varies between training and testing phases. Covariate shift, a specific instance of dataset shift, happens when only the input distribution changes. This problem is common in practical applications, influenced by factors such as experimental design bias and the unreliability of testing conditions during training. For example, email spam filters may struggle to identify new spam forms that differ from the training data. Despite its significance, dataset shift has historically received limited attention compared to related topics like semi-supervised and active learning. The chapters in this volume provide a mathematical and philosophical introduction to the issue, contextualize dataset shift within transfer learning, transduction, local learning, active learning, and semi-supervised learning, and present theoretical perspectives, including decision-theoretic and Bayesian views. Additionally, algorithms for addressing covariate shift are discussed.…
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- Title
- Dataset Shift in Machine Learning
- Author
- Joaquin Quinonero-Candela
- Publisher
- MIT Press
- Publication year
- 2022
- Condition
- As New
- Binding
- Softcover
- Language
- English
- ISBN 10
- 026254587X
- ISBN 13
- 9780262545877
- Seller catalogs
- Textbooks
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 Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama
"Synopsis" may belong to another edition of this title.
About the Author
Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
Masashi Sugiyama
Masashi Sugiyama is Director of the RIKEN Center for Advanced Intelligence Project and Professor of Computer Science at the University of Tokyo.
Anton Schwaighofer
Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K.
Neil D. Lawrence
Neil D. Lawrence is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester.
"About the title" may belong to another edition of this title.
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