Model selection under covariate shift

Masashi Sugiyama, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

A common assumption in supervised learning is that the training and test input points follow the same probability distribution. However, this assumption is not fulfilled, e.g., in interpolation, extrapolation, or active learning scenarios. The violation of this assumption-known as the covariate shift-causes a heavy bias in standard generalization error estimation schemes such as cross-validation and thus they result in poor model selection. In this paper, we therefore propose an alternative estimator of the generalization error. Under covariate shift, the proposed generalization error estimator is unbiased if the learning target function is included in the model at hand and it is asymptotically unbiased in general. Experimental results show that model selection with the proposed generalization error estimator is compared favorably to cross-validation in extrapolation.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages235-240
Number of pages6
Publication statusPublished - 2005
Externally publishedYes
Event15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005 - Warsaw, Poland
Duration: 2005 Sep 112005 Sep 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3697 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005
Country/TerritoryPoland
CityWarsaw
Period05/9/1105/9/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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