PREDICTION INTERVALS FOR SUPPORT VECTOR MACHINE REGRESSION

The support vector machine (SVM), first developed by Vapnik and his group at AT&T Bell Laboratories, is being used as a new technique for regression and classification problems. In this paper we present an approach to estimating prediction intervals for SVM regression based on posterior predictive densities. Furthermore, the method is illustrated with a data example.