Direct Estimation of Polynomial Densities in Generalized RBF Networks Using Moments

Authors:
Evangelos Dermatas
Affiliations:
-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

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Abstract

We present a direct estimation method of the output layer weights in a polynomial extension of the generalized radial-basis-function networks when used in pattern classification problems. The estimation is based on the L2-distance minimization of the density and the population moments. Each synaptic weight in the output layer is derived as a nonlinear function of the training data moments. The experimental results, using one- and two-dimensional simulated data and different polynomial orders, show that the classification rate of the polynomial densities is very close to the optimum rate.