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Conference Paper

A new discriminative kernel from probabilistic models

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Kawanabe M, Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DF07-F
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.