Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification

Balázs Kégl, Adam Krzyzak, Heinrich Niemann

Abstract: We apply complexity regularization to learn normalized radial basis function networks in nonparametric classification. We study convergence and the rates of convergence of the empirically trained networks and verify the results in computer experiments.