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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

Chapter:
(p.131) 8 Covariate Shift by Kernel Mean Matching
Source:
Dataset Shift in Machine Learning
Author(s):

Arthur Gretton

Alex Smola

Jiayuan Huang

Marcel Schmittfull

Karsten Borgwardt

Bernhard Schölkopf

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262170055.003.0008

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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