STRICTLY MODULAR PROBABILISTIC NEURAL NETWORKS FOR PATTERN RECOGNITION

摘要

Considering the statistical recognition of multidimensional binary observations we approximate the unknown class-conditional probability distributions by multivariate Bernoulli mixtures. We show that both the parameter optimization and the resulting Bayesian decision-making can be realized by a probabilistic neural network having strictly modular properties. In particular, the process of learning based on the EM algorithm can be performed by means of a sequential autonomous adaptation of neurons involving only the information from the input synapses and the interior of neurons. In this sense the probabilistic neural network can be designed automatically. The properties of the sequential strictly modular learning procedure are illustrated by numerical examples.