Gaussian based neural networks applied to pattern classification and multivariate probability density estimation

Author

Firmin, Christian ; Hamad, Denis

Author_Institution

Centre d´´Autom. de Lille, Villeneuve d´´Ascq, France

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2985

Abstract

A Gaussian based neural network is applied to the clustering problem. We consider the hypothesis that the samples are drawn from a finite mixture of Gaussian density functions. Each of them corresponds to one cluster. Competitive learning algorithms are then used to estimate the network parameters. The number of units in the hidden layer is determined by minimising the information criterion of Akaike. Performance evaluations using training data from mixture Gaussian densities are presented

Keywords

estimation theory; neural nets; optimisation; pattern classification; probability; unsupervised learning; Akaike information criterion; Gaussian based neural network; clustering; competitive learning; multivariate probability density estimation; parameter estimation; pattern classification; stochastic optimisation; unsupervised learning; Clustering algorithms; Covariance matrix; Density functional theory; Kernel; Neural networks; Parameter estimation; Pattern classification; Probability density function; Radial basis function networks; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374708

Filename

374708