Abstract

In this paper, the problem of implementing the reject option i n support vector machines (SVMs) is addressed. We started by observing that methods proposed so far simply apply a reject threshold to the outputs of a trained SVM. We then showed that, under the framework of the structural risk minimisation principle, the rejection region must be determined during the training phase of a classifier. By applying this concept, and by following Vapnik's approach, we developed a maximum margin classifier with reject option. This led us to a SVM whose rejection region is determined during the training phase, that is, a SVM with embedded reject option. To implement such a SVM, we devised a novel formulation of the SVM training problem and developed a specific algorithm to solve it. Preliminary results on a character recognition problem show the advantages of the proposed SVM i n terms of the achievable error-reject trade-off.

Citations