Minimal RBF networks by gaussian mixture model

Authors:
Sung Mahn Ahn;Sung Baik
Affiliations:
Kookmin University, Seoul, Korea;Sejong University, Seoul, Korea
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 5
Cited 0

Original Contribution: On the training of radial basis function classifiers

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A sequential learning scheme for function approximation using minimal radial basis function neural networks

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Fast learning in networks of locally-tuned processing units

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On convergence properties of the em algorithm for gaussian mixtures

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Abstract

Radial basis function (RBF) networks have been successfully applied to function interpolation and classification problems among others. In this paper, we propose a basis function optimization method using a mixture density model. We generalize the Gaussian radial basis functions to arbitrary covariance matrices, in order to fully utilize the Gaussian probability density function. We also try to achieve a parsimonious network topology by using a systematic procedure. According to experimental results, the proposed method achieved fairly comparable performance with smaller number of hidden layer nodes to the conventional approach in terms of correct classification rates.