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Dataset Shift in Machine Learning

Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence (eds)

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
book
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.001.0001
Subject:
Computer Science, Machine Learning

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 ... More


When Training and Test Sets Are Different: Characterizing Learning Transfer

Storkey Amos

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0001
Subject:
Computer Science, Machine Learning

This chapter introduces the general learning transfer problem and formulates it in terms of a change of scenario. Standard regression and classification models can be characterized as conditional ... More


Conclusions and Future Prospects

Masashi Sugiyama and Motoaki Kawanabe

in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.003.0011
Subject:
Computer Science, Machine Learning

This chapter summarizes the main themes covered in the preceding discussions and discusses future prospects. This book has provided a comprehensive overview of theory, algorithms, and applications of ... More


Introduction and Problem Formulation

Masashi Sugiyama and Motoaki Kawanabe

in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.003.0001
Subject:
Computer Science, Machine Learning

This chapter provides an introduction to covariate shift adaptation toward machine learning in a non-stationary environment. It begins by discussing cover machine learning under covariate shift. It ... More


Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Masashi Sugiyama and Motoaki Kawanabe

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
book
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.001.0001
Subject:
Computer Science, Machine Learning

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the ... More


On Bayesian Transduction: Implications for the Covariate Shift Problem

Kai Hansen Lars

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0004
Subject:
Computer Science, Machine Learning

This chapter analyzes Bayesian supervised learning with extensions to semisupervised learning, and learning with covariate or dataset shift. The main result is an expression for the generalization ... More


Geometry of Covariate Shift with Applications to Active Learning

Kanamori Takafumi and Shimodaira Hidetoshi

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0006
Subject:
Computer Science, Machine Learning

This chapter examines learning algorithms under the covariate shift in which training and test data are drawn from different distributions. Using a naive estimator under the covariate shift, such as ... More


Applications of Covariate Shift Adaptation

Masashi Sugiyama and Motoaki Kawanabe

in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.003.0007
Subject:
Computer Science, Machine Learning

This chapter discusses state-of-the-art applications of covariate shift adaptation techniques to various real-world problems. It covers non-stationarity adaptation in brain-computer interfaces; ... More


Author Comments

Shimodaira Hidetoshi, Sugiyama Masashi, Storkey Amos, Gretton Arthur, and David Shai-Ben

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0011
Subject:
Computer Science, Machine Learning

This chapter presents comments by the authors about dataset shift in machine learning. Topics covered include covariate shift and misspecification; and whether importance weighting is needed under ... More


Covariate Shift by Kernel Mean Matching

Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0008
Subject:
Computer Science, Machine Learning

This chapter addresses the problem of distribution matching between training and test stages. It proposes a method called kernel mean matching, which allows direct estimation of the importance weight ... More


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