Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

被引：0

作者：

Neruda, Roman ^{[1
]}

Vidnerova, Petra ^{[1
]}

机构：

[1] Acad Sci Czech Republ, Inst Comp Sci, Pod Vodarenskou Vezi 2, Prague 8, Czech Republic

来源：

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING | 2009年 / 2卷 / 01期

关键词：

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Regularization theory presents a sound framework to solving supervised learning problems. However, there is a gap between the theoretical results and practical suitability of regularization networks (RN). Radial basis function networks (RBF) that can be seen as a special case of regularization networks have a rich selection of learning algorithms. In this work we study a relationship between RN and RBF, and show that theoretical estimates for RN hold for a concrete RBF applied to real-world data, to a certain degree. This can provide several recommendations for strategies on choosing number of units in RBF network.

引用

页码：49 / 57

页数：9

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