An evolutionary clustering technique with local search to design RBF neural network classifiers

De Castro, Leandro N., Hruschka, Eduardo R., and Campello, Ricardo J.G.B. (2004) An evolutionary clustering technique with local search to design RBF neural network classifiers. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, pp. 2083-2088. From: 2004 IEEE International Joint Conference on Neural Networks, 25-29 July 2004, Budapest, Hungary.

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Abstract

Radial basis function neural networks constitute one type of feedforward neural net that requires a suitable determination of the basis functions so as to work properly. Among the many approaches available in the literature, the one proposed here combines a clustering genetic algorithm with K-means to automatically select the number and location of basis functions to be used in the RBF network. Preliminary simulation results suggest that the proposed hybrid algorithm can be successfully applied to classification problems, leading to parsimonious solutions, with competitive classification rates, when compared with other approaches from the RBF literature.

Item ID: 47603
Item Type: Conference Item (Research - E1)
ISBN: 978-0-7803-8359-3
Date Deposited: 08 Mar 2017 07:40
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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