Support Vector Machines as Probabilistic Models

Franc, V.; Zien, A.; Schölkopf, B.

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Support Vector Machines as Probabilistic Models

Franc, V., Zien, A., & Schölkopf, B. (2011). Support Vector Machines as Probabilistic Models. In 28th International Conference on Machine Learning (ICML 2011) (pp. 665-672).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-4CAD-5 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0028-51D9-1

Genre: Conference Paper

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Creators:
Franc, V., Author
Zien, A.¹, Author
Schölkopf, B.², Author

Affiliations:
1Max Planck Society, ou_persistent13
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647

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Free keywords: MPI für Intelligente Systeme; Abt. Schölkopf;

Abstract: We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic models. This model class can be viewed as a reparametrization of the SVM in a similar vein to the v-SVM reparametrizing the classical (C-)SVM. It is not discriminative, but has a non-uniform marginal. We illustrate the benefits of this new view by rederiving and re-investigating two established SVM-related algorithms.

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Dates: Date issued: 2011-07-01

Publication Status: Issued

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Identifiers: eDoc: 596823
URI: http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/2011/ICML-2011-Franc.pdf
Other: FrancZS2011

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Title: 28th International Conference on Machine Learning (ICML 2011)

Source Genre: Proceedings

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Pages: 7 Volume / Issue: - Sequence Number: - Start / End Page: 665 - 672 Identifier: -