Paper Info

Title | ||
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Radial basis function networks and complexity regularization in function learning. |

Abstract | ||
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In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from previous complexity regularization neural-network function learning schemes in that we operate with random covering numbers and l(1) metric entropy, making it possible to consider much broader families of activation functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization involving empirical risk minimization. Bounds on the expected risk in terms of the sample size are obtained for a large class of loss functions. Rates of convergence to the optimal loss are also derived. |

Year | DOI | Venue |
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1996 | 10.1109/72.661120 | IEEE Transactions on Neural Networks |

Keywords | DocType | Volume |

empirical risk minimization,nonlinear function estimation,expected risk,radial basis function network,loss function,previous complexity regularization neural-network,complexity regularization,activation function,network parameter,basis function network,estimation bound,function learning | Conference | 9 |

Issue | ISSN | Citations |

2 | 1045-9227 | 24 |

PageRank | References | Authors |

1.83 | 12 | 2 |

Authors (2 rows)

Cited by (24 rows)

References (12 rows)

Name | Order | Citations | PageRank |
---|---|---|---|

A. Krzyzak | 1 | 585 | 63.11 |

Linder, Tamás | 2 | 24 | 1.83 |