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



ID: 352330.0, MPI für biologische Kybernetik / Biologische Kybernetik
A Hilbert Space Embedding for Distributions
Authors:Smola, A.; Gretton, A.; Song, L.; Schölkopf, B.
Editors:Hutter, M.; Servedio, R. A.; Takimoto, E.; Takimoto, Hutter, M. , R. A. Servedio, E.
Date of Publication (YYYY-MM-DD):2007-10
Title of Proceedings:Algorithmic Learning Theory
Start Page:13
End Page:31
Physical Description:19
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:We describe a technique for comparing distributions without
the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the
same distribution, covariate shift correction, local learning, measures of independence, and density estimation.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:4645
URL:http://www.springerlink.com/content/l3u64167507j52...
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