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Support Vector Machines as Probabilistic Models

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84331

Zien,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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). Madison, WI, USA: International Machine Learning Society.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BB30-F
Zusammenfassung
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.