In this paper, we study the incorporation of the support vector machine (SVM) into the (hierarchical) mixture of experts model to form a support vector mixture. We show that, in both classification and regression problems, the use of a support vector mixture leads to quadratic programming (QP) problems that are very similar to those for a SVM, with no increase in the dimensionality of the QP problems. Moreover, a support vector mixture, besides allowing for the use of different experts in

more »

... ent regions of the input space, also supports easy combination of different architectures such as polynomial networks and radial basis function networks. 1 As we will see in later sections, this is done by changing the kernel function of the SVM.