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  A new discriminative kernel from probabilistic models

Tsuda, K., Kawanabe M, Rätsch, G., Sonnenburg, S., & Müller, K.-R. (2002). A new discriminative kernel from probabilistic models. Advances in Neural Information Processing Systems, 977-984.

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 Creators:
Tsuda, K1, Author           
Kawanabe M, Rätsch, G1, Author           
Sonnenburg, S, Author
Müller, K-R, Author
Dietterich, Editor
T.G., Editor
Becker, S., Editor
Ghahramani, Z., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Recently, Jaakkola and Haussler proposed a method for constructing kernel functions from probabilistic models. Their so called \Fisher kernel" has been combined with discriminative classi ers such as SVM and applied successfully in e.g. DNA and protein analysis. Whereas the Fisher kernel (FK) is calculated from the marginal log-likelihood, we propose the TOP kernel derived from Tangent vectors Of Posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments our new discriminative TOP kernel compares favorably to the Fisher kernel.

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 Dates: 2002-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-04208-8
URI: http://books.nips.cc/nips14.html
BibTex Citekey: 2191
 Degree: -

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Title: Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001)
Place of Event: Vancouver, BC, Canada
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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 977 - 984 Identifier: -