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  Covariate Shift by Kernel Mean Matching

Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., & Schölkopf, B. (2009). Covariate Shift by Kernel Mean Matching. In J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, & N. Lawrence (Eds.), Dataset Shift in Machine Learning (pp. 131-160). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C5D5-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-09AD-1
Genre: Book Chapter

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 Creators:
Gretton, A1, 2, Author              
Smola, AJ1, 2, Author              
Huang, J1, 2, Author              
Schmittfull, M1, 2, Author              
Borgwardt, KM1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: 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.

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 Dates: 2009
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 5376
DOI: 10.7551/mitpress/9780262170055.003.0008
 Degree: -

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Title: Dataset Shift in Machine Learning
Source Genre: Book
 Creator(s):
Quiñonero-Candela, J, Editor
Sugiyama, M, Editor
Schwaighofer, A, Editor
Lawrence, ND, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: 8 Start / End Page: 131 - 160 Identifier: ISBN: 978-0-262-17005-5