Nonparametric classification using radial basis function nets and empirical risk minimization

In the paper convergence properties of radial basis function (RBF) networks are studied for a large class of basis functions. The universal approximation property of the nets is shown. Parameters of RBF nets are learned through empirical risk minimization. The optimal nets are shown to be consistent in nonparametric classification. The tools used in the analysis include Vapnik-Chervonenkis (VC) dimension and the covering numbers.

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