Importance-weighted cross-validation for covariate shift

Masashi Sugiyama, Benjamin Blankertz, Matthias Krauledat, Guido Dornhege, Klaus Robert Müller

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

14 Citations (Scopus)


A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points used for testing. However, this assumption is not satisfied, for example, when the outside of training region is extrapolated. The situation where the training input points and test input points follow different distributions is called the covariate shift. Under the covariate shift, standard machine learning techniques such as empirical risk minimization or cross-validation do not work well since their unbiasedness is no longer maintained. In this paper, we propose a new method called importance-weighted cross-validation, which is still unbiased even under the covariate shift. The usefulness of our proposed method is successfully tested on toy data and furthermore demonstrated in the brain-computer interface, where strong non-stationarity effects can be seen between calibration and feedback sessions.

Original languageEnglish
Title of host publicationPattern Recognition - 28th DAGM Symposium, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540444122, 9783540444121
Publication statusPublished - 2006
Externally publishedYes
Event28th Symposium of the German Association for Pattern Recognition, DAGM 2006 - Berlin, Germany
Duration: 2006 Sept 122006 Sept 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4174 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other28th Symposium of the German Association for Pattern Recognition, DAGM 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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