Radial basis function networks and nonparametric classification: complexity regularization and rates of convergence

The method of complexity regularization is applied to one hidden-layer radial basis function networks to derive regression estimation bounds and convergence rates for classification. Bounds on the expected risk in terms of the training sample size are obtained for a large class of activation functions, namely functions of bounded variation. Rates of convergence to the optimal loss are also derived.