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Conference Paper

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

MPS-Authors
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Hein,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lal,  TN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83824

Bousquet,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://link.springer.com/content/pdf/10.1007%2F978-3-540-28649-3_33.pdf
(Publisher version)

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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F399-5
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.