OLS versus SVM approach to learning of RBF networks

The paper presents the comparative analysis of the learning algorithms of the radial basis function (RBF) neural networks. Two best adaptive algorithms are considered. One is based on the orthogonal least square (OLS) applying Gram-Schmidt orthogonalization and the second is relying on the support vector machine (SVM) approach. The results of numerical experiments in the classification and regression modes are presented and discussed in the paper.