BAYESNET: Bayesian classification network based on biased random competition using Gaussian kernels

A new neural network architecture referred to as BAYESNET (Bayesian network) is presented. BAYESNET is capable of learning the probability density functions (PDFs) of individual pattern classes from a collection of learning samples, and designed for pattern classification based on the Bayesian decision rule. In BAYESNET, the PDF of a class is represented in terms of the sum of Gaussian subclass PDFs with unknown means, covariances and subclass probabilities that are to be determined through learning. The unique feature of learning the PDF of a class in BAYESNET is the random assignment of a sample of a class to subclasses, i.e., a sample is randomly assigned to a particular subclass for learning according to the probability of the sample to belong to individual subclasses. The property of Gaussian function provides efficient learning of parameters. It is shown that the learned parameters agree with those obtained by the maximum likelihood estimation of the sample set