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  Hilbertian Metrics on Probability Measures and their Application in SVM's

Hein, H., Lal, T., & Bousquet, O. (2004). Hilbertian Metrics on Probability Measures and their Application in SVM's. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 270-277). Berlin, Germany: Springer.

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 Creators:
Hein, H1, 2, Author           
Lal, TN1, 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: The goal of this article is to
investigate the field of Hilbertian metrics on probability
measures. Since they are very versatile and can therefore be
applied in various problems they are of great interest in kernel
methods. Quit recently Topsoe and Fuglede introduced a family
of Hilbertian metrics on probability measures. We give basic
properties of the Hilbertian metrics of this family and other used
metrics in the literature. Then we propose an extension of the
considered metrics which incorporates structural information of
the probability space into the Hilbertian metric. Finally we
compare all proposed metrics in an image and text classification
problem using histogram data.

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 Dates: 2004-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2786
DOI: 10.1007/978-3-540-28649-3_33
 Degree: -

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Title: 26th Annual Symposium of the German Association for Pattern Recognition (DAGM 2004)
Place of Event: Tübingen, Germany
Start-/End Date: 2004-08-30 - 2004-09-01

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Title: Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004
Source Genre: Proceedings
 Creator(s):
Rasmussen, CE1, Editor           
Bülthoff, HH1, Editor           
Schölkopf, B1, Editor           
Giese, MA, Editor           
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 270 - 277 Identifier: ISBN: 978-3-540-22945-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 3175 Sequence Number: - Start / End Page: - Identifier: -