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  Tailoring density estimation via reproducing kernel moment matching

Song, L., Zhang, X., Smola, A., Gretton, A., & Schölkopf, B. (2008). Tailoring density estimation via reproducing kernel moment matching. In W. Cohen, A. McCallum, & S. Roweis (Eds.), ICML '08: Proceedings of the 25th international conference on Machine learning (pp. 992-999). New York, NY, USA: ACM Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C843-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-431B-4
Genre: Conference Paper

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 Creators:
Song, L, Author
Zhang, X, Author
Smola, A, Author              
Gretton, A1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2008-07
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Degree: -

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Title: 25th International Conference on Machine Learning (ICML 2008)
Place of Event: Helsinki, Finland
Start-/End Date: 2008-07-05 - 2008-07-08

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Title: ICML '08: Proceedings of the 25th international conference on Machine learning
Source Genre: Proceedings
 Creator(s):
Cohen, WW, Editor
McCallum, A, Editor
Roweis, ST, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 992 - 999 Identifier: ISBN: 978-1-60558-205-4