A Bayesian framework for regularized SVM parameter estimation

The support vector machine (SVM) is considered here in the context of pattern classification. The emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of the learning samples that we call the support set and then determines not only the weights of the classifier but also the hyperparameter that controls the influence of the regularizing penalty term on basis thereof. We provide numerical results using several data sets from the public domain.