Robust Twin Parametric Margin Support Vector Machine for Multiclass
  Classification
Renato De Leone Francesca Maggioni Andrea Spinelli
In this paper we present a Twin Parametric-Margin Support Vector Machine
(TPMSVM) model to tackle the problem of multiclass classification. In the
spirit of one-versus-all paradigm, for each class we construct a classifier by
solving a TPMSVM-type model. Once all classifiers have been determined, they
are combined into an aggregate decision function. We consider the cases of both
linear and nonlinear kernel-induced classifiers. In addition, we robustify the
proposed approach through robust optimization techniques. Indeed, in real-world
applications observations are subject to measurement errors and noise,
affecting the quality of the solutions. Consequently, data uncertainties need
to be included within the model in order to prevent low accuracies in the
classification process. Preliminary computational experiments on real-world
datasets show the good performance of the proposed approach.