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

    16 Citations (Scopus)

    Abstract

    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techniques such as empirical risk minimization or cross-validation do not work well since their unbiasedness is no longer maintained.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
    Pages354-363
    Number of pages10
    ISBN (Print)3540444122, 9783540444121
    DOIs
    Publication statusPublished - 2006
    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

    Other

    Other28th Symposium of the German Association for Pattern Recognition, DAGM 2006
    Country/TerritoryGermany
    CityBerlin
    Period06/9/1206/9/14

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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