SVM regularizer models on RKHS vs. on R m

Abstract

There are two types of regularizer for SVM. The most popular one is that the classification function is norm-regularized on a Reproduced Kernel Hilbert Space(RKHS), and another important model is generalized support vector machine(GSVM), in which the coefficients of the classification function is norm-regularized on a Euclidean space R m . In this paper, we analyze the difference between them on computing stability, computational complexity and the efficiency of the Newton-type algorithms. Many typical loss functions are considered. The results show that the model of GSVM has more advantages than the other model. Some experiments support our analysis. © 2012 Springer-Verlag.