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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents

ID: 461839.0, MPI für biologische Kybernetik / Biologische Kybernetik
Covariate Shift by Kernel Mean Matching
Authors:Gretton, A.; Smola, A.J.; Huang, J.; Schmittfull, M.; Borgwardt, K.M.; Schölkopf, B.
Editors:Candela, J. Quiñonero; Sugiyama, M.; Schwaighofer, A.; Lawrence, N. D.
Place of Publication:Cambridge, MA, USA
Publisher:MIT Press
Date of Publication (YYYY-MM-DD):2009-02
Title of Book:Dataset Shift in Machine Learning
Start Page:131
End Page:160
Physical Description:30
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Given sets of observations of training and test data, we consider the problem of
re-weighting the training data such that its distribution more closely matches that
of the test data. We achieve this goal by matching covariate distributions between
training and test sets in a high dimensional feature space (specifically, a reproducing
kernel Hilbert space). This approach does not require distribution estimation.
Instead, the sample weights are obtained by a simple quadratic programming
procedure. We provide a uniform convergence bound on the distance between
the reweighted training feature mean and the test feature mean, a transductive
bound on the expected loss of an algorithm trained on the reweighted data, and
a connection to single class SVMs. While our method is designed to deal with the
case of simple covariate shift (in the sense of Chapter ??), we have also found
benefits for sample selection bias on the labels. Our correction procedure yields
its greatest and most consistent advantages when the learning algorithm returns a
classifier/regressor that is simpler" than the data might suggest.
Comment of the Author/Creator:ISBN: 978-0-262-17005-5
External Publication Status:published
Document Type:InBook
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
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