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Dataset Shift in Machine Learning$
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Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence

Print publication date: 2008

Print ISBN-13: 9780262170055

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262170055.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 23 October 2021

A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift

A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift

Chapter:
(p.106) (p.107) 7 A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift
Source:
Dataset Shift in Machine Learning
Author(s):

Sugiyama Masashi

Rubens Neil

Müller Klaus-Robert

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262170055.003.0007

This chapter, which addresses the problems of model selection and active learning in the conditional expectation framework, is organized as follows. Section 7.2 formulates a linear regression problem with covariate shift. Sections 7.3 and 7.4 introduce a model selection criterion and an active learning criterion, respectively, in the conditional expectation framework and show that they are more advantageous than the full expectation methods in the context of approximate linear regression. Section 7.5 discusses how model selection and active learning can be combined. Concluding remarks and future prospects are given in Section 7.6.

Keywords:   model selection, active learning, conditional expectation analysis, covariate shift

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