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  A Hilbert Space Embedding for Distributions

Smola, A., Gretton, A., Song, L., & Schölkopf, B. (2007). A Hilbert Space Embedding for Distributions. In V. Corruble, M. Takeda, & E. Suzuki (Eds.), Discovery Science: 10th International Conference, DS 2007 Sendai, Japan, October 1-4, 2007 (pp. 40-41). Berlin, Germany: Springer.

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 Creators:
Smola, AJ, Author
Gretton, A1, 2, Author              
Song, L, 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: While kernel methods are the basis of many popular techniques in supervised learning, they are less commonly used in testing, estimation, and analysis of probability distributions, where information theoretic approaches rule the roost. However it becomes difficult to estimate mutual information or entropy if the data are high dimensional.

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 Dates: 2007-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-75488-6_5
BibTex Citekey: 4644
 Degree: -

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Title: 10th International Conference on Discovery Science (DS 2007)
Place of Event: Sendai, Japan
Start-/End Date: 2007-10-01 - 2007-10-04

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Title: Discovery Science: 10th International Conference, DS 2007 Sendai, Japan, October 1-4, 2007
Source Genre: Proceedings
 Creator(s):
Corruble, V, Editor
Takeda, M, Editor
Suzuki, E, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 40 - 41 Identifier: ISBN: 978-3-540-75487-9

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 4755 Sequence Number: - Start / End Page: - Identifier: -