A Bayesian classifier by using the merging RBF networks
Minghu Jiang, Georges E. Gielen, Beixing Deng, Xiaofang Tang, Qiuqi Ruan, Baozong Yuan · 2003
In paper we propose a Bayesian classifier for the multiclass problem by using the merging RBF networks. The estimation of probability density function (PDF) uses a Gaussian mixture model updated with the EM algorithm. The centers and variances of RBF networks are gradually updated to merge the basis united by the supervised gradient descent of the error energy function. The algorithms are used to construct the RBF networks and to reduce the number of basis units. The experimental simulations show the validity of the proposed method.