SVM-based classifier design with controlled confidence

A new classification methodology with controlled error rates and a reject option is proposed in this paper. The proposed methodology is implemented using support vector machine´s (SVM´s) posterior probability preserving property. A new nonparametric method is proposed to accurately estimate error rates from the output of a trained SVM. The experimental results clearly demonstrate the efficacy of the suggested classifier design methodology.