Nonlinear function learning by the normalized radial basis function networks

Authors:
Adam Krzyżak;Dominik Schäfer
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada;Dominik Schäfer, Fachbereich Mathematik, Universität Stuttgart, Stuttgart, Germany
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

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Abstract

We study strong universal consistency and the rates of convergence of nonlinear regression function learning algorithms using normalized radial basis function networks. The parameters of the network including centers, covariance matrices and synaptic weights are trained by the empirical risk minimization. We show the rates of convergence for the networks whose parameters are learned by the complexity regularization.