Support Vector Machines as Probabilistic Models

Franc, V; Zien, A; Schölkopf, B

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Support Vector Machines as Probabilistic Models

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Schölkopf, B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BB30-F

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.