On L1 convergence rate of RBF networks and kernel regression estimators with applications in classification

Studies the convergence properties of the mean integrated absolute error (MIAE) for kernel regression estimators (KRE) and radial basis function (RBF) nets. The authors show that the MIAE of KRE and RBF nets converges to zero as the size of network and the size of training sequence tend to infinity, and the authors give the upper bound for the convergence rate for approximating functions satisfying Lipschitz condition of order α,0<α⩽1. The obtained results are applied to nonparametric classification