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  Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., & Bousquet, O. (2005). Hilbertian Metrics and Positive Definite Kernels on Probability Measures. In R. Cowell, & Z. Ghahramani (Eds.), AISTATS 2005: Tenth International Workshop onArtificial Intelligence and Statistics (pp. 136-143). The Society for Artificial Intelligence and Statistics.

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 Creators:
Hein, M1, 2, Author           
Bousquet, O1, 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 investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and two image classification problems.

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 Dates: 2005-01
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3013
 Degree: -

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Title: Tenth International Workshop on Artificial Intelligence and Statistics (AI Statistics 2005)
Place of Event: Barbados
Start-/End Date: 2005-01-06 - 2005-01-08

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Title: AISTATS 2005: Tenth International Workshop onArtificial Intelligence and Statistics
Source Genre: Proceedings
 Creator(s):
Cowell, R, Editor
Ghahramani, Z, Editor
Affiliations:
-
Publ. Info: The Society for Artificial Intelligence and Statistics
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 136 - 143 Identifier: ISBN: 0-9727358-1-X