Support Vector Machines as Probabilistic Models

Vojtech Franc (CTU Prague, Czech Republic)

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 $\nu$-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 re-deriving and re-investigating two established SVM-related algorithms.