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  A Kernel Approach to Comparing Distributions

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., & Smola, A. (2007). A Kernel Approach to Comparing Distributions. In Twenty-Second AAAI Conference on Artificial Intelligence (IAAI-07) (pp. 1637-1641). Menlo Park, CA, USA: AAAI Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CCB1-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-D820-5
Genre: Conference Paper

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 Creators:
Gretton, A1, 2, Author              
Borgwardt, KM, Author              
Rasch, M, Author              
Schölkopf, B1, 2, Author              
Smola, AJ, 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, 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. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

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 Dates: 2007-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4426
 Degree: -

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Title: Twenty-Second AAAI Conference on Artificial Intelligence (IAAI-07)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2007-07-22 - 2007-07-26

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Title: Twenty-Second AAAI Conference on Artificial Intelligence (IAAI-07)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Menlo Park, CA, USA : AAAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1637 - 1641 Identifier: ISBN: 978-1-577-35323-2