Hybrid training of radial basis function networks in a partitioning context of classification

The design of radial basis function networks (RBF) is rather complex because of the great number of parameters that must be adjusted : positioning and number of kernels, choice of the distance type and centre widths, weight values. This article details these points in the framework of classification tasks with a partitioning approach : the global K-class problem is split into K 2-class sub-problems. An adaptation of the Orthogonal Least Square method is presented in order to select the centres of each sub-classifier in connection with a particular stopping criterion based on the addition of a random centre. Moreover, different choices of distance and centre widths are compared and illustrated by a 4-class problem in the Non Destructive Evaluation domain.