On Learning and Convergence of RBF Networks in Regression Estimation and Classification

摘要

In the paper we study convergence of the RBF networks with so-called regular radial kernels. The parameters of the network are learned by the empirical risk minimization. Mean square convergence of L_2 error is investigated using the machine learning tools such as VC dimension and covering numbers. RBF network estimates are applied in nonlinear function learning and classification.