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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 M. Hutter, R. Servedio, & E. Takimoto (Eds.), Algorithmic Learning Theory: 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007 (pp. 13-31). Berlin, Germany: Springer.

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 Creators:
Smola, A, 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: 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.

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 Dates: 2007-10
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-75225-7_5
BibTex Citekey: 4645
 Degree: -

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Title: 18th International Conference on Algorithmic Learning Theory (ALT 2007)
Place of Event: Sendai, Japan
Start-/End Date: 2007-10-01 - 2007-10-04

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Title: Algorithmic Learning Theory: 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007
Source Genre: Proceedings
 Creator(s):
Hutter, M, Editor
Servedio, RA, Editor
Takimoto, E, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 13 - 31 Identifier: ISBN: 978-3-540-75224-0

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 4754 Sequence Number: - Start / End Page: - Identifier: -