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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 Pattern Recognition, Proceedings of th 26th DAGM Symposium (pp. 270-277).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-F399-5 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-F39A-3
Genre: Conference Paper

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 Creators:
Hein, H1, Author           
Lal, TN1, Author           
Bousquet, O1, Author           
Rasmussen, Editor
E., C., Editor
Bülthoff, H. H., Editor
Giese, M., Editor
Schölkopf, B., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2786
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Title: Pattern Recognition, Proceedings of th 26th DAGM Symposium
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Title: Pattern Recognition, Proceedings of th 26th DAGM Symposium
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 270 - 277 Identifier: -