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list price: $42.00 USD
edition:Hardcover
category: Computers
published: Dec 2008
ISBN:9780262170055
publisher: The MIT Press

Dataset Shift in Machine Learning

edited by Anton Schwaighofer; Joaquin Quiñonero-Candela; Neil D. Lawrence, contributions by Takafumi Kanamori; Klaus-Robert Müller; Michael Brückner; Marcel Schmittfull; Alexander J. Smola; David Corfield; Choon Hui Teo; Matthias Hein; Tobias Scheffer; Neil Rubens; Karsten Borgwardt; Bernhard Schölkopf; Jiayuan Huang; Steffen Bickel; Arthur Gretton; Hidetoshi Shimodaira; Amos Storkey; Shai Ben-David; Lars Kai Hansen; Amir Globerson; Masashi Sugiyama & Sam Roweis

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intelligence (ai) & semantics, data processing
0 of 5
0 ratings
rated!
rated!
list price: $42.00 USD
edition:Hardcover
category: Computers
published: Dec 2008
ISBN:9780262170055
publisher: The MIT Press
Description

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 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

About the Authors
Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K.
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Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K.
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Klaus-Robert Müller 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.
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Klaus-Robert Müller 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.
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Klaus-Robert Müller 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.
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Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.
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Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.
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Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
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Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
Author profile page >

Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
Author profile page >

Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
Author profile page >

Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
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Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
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Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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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Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.
Author profile page >

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.
Author profile page >

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.
Author profile page >

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.
Author profile page >

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.
Author profile page >

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.
Author profile page >

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.
Author profile page >

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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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.
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Recommended Age, Grade, and Reading Levels
Age:
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Grade:
13 to 17

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