Regularization in the selection of radial basis function centers

  • Authors:
  • Mark J. L. Orr

  • Affiliations:
  • Centre for Cognitive Science, University of Edinburgh, 2, Buccleuch Place, Edinburgh EH8 9LW, UK

  • Venue:
  • Neural Computation
  • Year:
  • 1995

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Abstract

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.