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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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Covariate Shift by Kernel Mean Matching

Covariate Shift by Kernel Mean Matching

(p.131) 8 Covariate Shift by Kernel Mean Matching
Dataset Shift in Machine Learning

Arthur Gretton

Alex Smola

Jiayuan Huang

Marcel Schmittfull

Karsten Borgwardt

Bernhard Schölkopf

The MIT Press

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 without going through density estimation. The chapter then relates the re-weighted estimation approaches to local learning, where labels on test data are estimated given a subset of training data in a neighborhood of the test point. Examples are nearest-neighbor estimators and Watson–Nadaraya-type estimators. The chapter also provides detailed proofs concerning the statistical properties of the kernel mean matching estimator, and detailed experimental analyses for both covariate shift and local learning.

Keywords:   distribution matching, training, test stage, kernel mean matching, risk estimates, covariate shift, local learning

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