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



ID: 420051.0, MPI für biologische Kybernetik / Biologische Kybernetik
Tailoring density estimation via reproducing kernel moment matching
Authors:Song, L.; Zhang, X.; Smola, A.; Gretton, A.; Schölkopf, B.
Editors:Cohen, W. W.; McCallum, A.; Roweis, S.
Date of Publication (YYYY-MM-DD):2008-07
Title of Proceedings:Proceedings of the 25th International Conference on Machine Learning (ICML 2008)
Start Page:992
End Page:999
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Moment matching is a popular means of parametric
density estimation. We extend this technique
to nonparametric estimation of mixture
models. Our approach works by embedding
distributions into a reproducing kernel Hilbert
space, and performing moment matching in that
space. This allows us to tailor density estimators
to a function class of interest (i.e., for which
we would like to compute expectations). We
show our density estimation approach is useful
in applications such as message compression in
graphical models, and image classification and
retrieval.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
Identifiers:LOCALID:5155
URL:http://icml2008.cs.helsinki.fi/papers/icml2008proc...
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