REGULARIZATION IN THE SELECTION OF RADIAL BASIS FUNCTION CENTERS

Subset selection and regularization are two well-known techniques that can improve the generalization performance of nonparametric linear regression estimators, such as radial basis function networks. This paper examines regularized forward selection (RFS)-a combination of forward subset selection and zero-order regularization. An efficient implementation of RFS into which either delete-1 or generalized cross-validation can be incorporated and a reestimation formula for the regularization parameter are also discussed. Simulation studies are presented that demonstrate improved generalization performance due to regularization in the forward selection of radial basis function centers.