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An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. 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 Bruckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Muller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schoelkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama
Biografía del autor
Joaquin Quinonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K. Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology. Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K. 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. Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology. Klaus-Robert Muller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin. Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra. Bernhard Schoelkopf is Director at the Max Planck Institute for Intelligent Systems in Tubingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
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- VerlagThe MIT Press
- Erscheinungsdatum2008
- ISBN 10 0262170051
- ISBN 13 9780262170055
- EinbandTapa dura
- SpracheEnglisch
- Anzahl der Seiten248
- HerausgeberQuinonero-Candela Joaquin, Sugiyama Masashi, Schwaighofer Anton, Lawrence Neil D.
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Dataset Shift in Machine Learning
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