An algorithm of estimating the generalization performance of RBF-SVM

Abstract

Using the sparseness of a support vector machine (SVM) solution, properties of radial basis function (RBF) kernel and the inter-median parameters in training the SVM, an algorithm to estimate the generalization performance of RBF-SVM is presented. Without additional complex computing, it overcomes many disadvantages of existing algorithm such as longer computation time and narrower application range. It is proved to be a general method for estimating the generalization performance of a RBF-SVM theoretically and experimentally and can be applied in wide range problems of pattern recognition using SVM.