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  Support Vector Machines as Probabilistic Models

Franc, V., Zien, A., & Schölkopf, B. (2011). Support Vector Machines as Probabilistic Models. In L. Getoor, & T. Scheffer (Eds.), 28th International Conference on Machine Learning (ICML 2011) (pp. 665-672). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BB30-F Version Permalink: https://hdl.handle.net/21.11116/0000-0002-0AA0-E
Genre: Conference Paper

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http://www.icml-2011.org/ (Table of contents)
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 Creators:
Franc, V, Author
Zien, A, Author           
Schölkopf, B1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 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: 2011-07
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: FrancZS2011
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Title: 28th International Conference on Machine Learning (ICML 2011)
Place of Event: Bellevue, WA, USA
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Title: 28th International Conference on Machine Learning (ICML 2011)
Source Genre: Proceedings
 Creator(s):
Getoor, L, Editor
Scheffer, T, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 665 - 672 Identifier: ISBN: 978-1-450-30619-5