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.