ML Anthology

Regularization in the Selection of Radial Basis Function Centers

Mark J. L. Orr
NeCo 1995 pp. 606-623
doi:10.1162/NECO.1995.7.3.606 /neco/1995/orr1995neco-regularization/

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.

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Cite

Text

Orr. "Regularization in the Selection of Radial Basis Function Centers." Neural Computation, 1995. doi:10.1162/NECO.1995.7.3.606

Markdown

[Orr. "Regularization in the Selection of Radial Basis Function Centers." Neural Computation, 1995.](https://mlanthology.org/neco/1995/orr1995neco-regularization/) doi:10.1162/NECO.1995.7.3.606

BibTeX

@article{orr1995neco-regularization,
  title     = {{Regularization in the Selection of Radial Basis Function Centers}},
  author    = {Orr, Mark J. L.},
  journal   = {Neural Computation},
  year      = {1995},
  pages     = {606-623},
  doi       = {10.1162/NECO.1995.7.3.606},
  volume    = {7},
  url       = {https://mlanthology.org/neco/1995/orr1995neco-regularization/}
}