Model selection for multi-class SVMs
Abstract
In the framework of statistical learning, fitting a model to a given problem is usually done in two steps. First, model selection is performed, to set the values of the hyperparameters. Second, training results in the selection, for this set of values, of a function performing satisfactorily on the problem. Choosing the values of the hyperparameters remains a difficult task, which has only been addressed so far in the case of bi-class SVMs. We derive here a solution dedicated to M-SVMs. It is based on a new bound on the risk of large margin classifiers.