In this paper, the problem of implementing the reject option in 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 in terms of the achievable error-reject trade-off.
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|Titolo:||Support Vector Machines with Embedded Reject Option|
|Data di pubblicazione:||2002|
|Citazione:||Support Vector Machines with Embedded Reject Option / FUMERA G; ROLI F. - LNCS 2388(2002), pp. 68-82. ((Intervento presentato al convegno Int. Workshop on Pattern Recognition with Support Vector Machines (SVM2002) tenutosi a Niagara Falls, Canada nel August 10, 2002.|
|Tipologia:||4.1 Contributo in Atti di convegno|