AN EFFICIENT EM TRAINING ALGORITHM FOR PROBABILITY MAPPING NETWORKS
一种概率映射网络的EM训练算法

An Expectation-Maximization(EM) training algorithm for estimating the parameters of a special Probability Mapping Network (PMN) structure which forms a multicatolog Bayes classifier is proposed in this paper. The structure of PMN is a four-layer Feedforward Neural Networks(FNN), where the Gaussian probability density function is realized as an internal node. In this way, the EM algorithm is extended to deal with supervised learning of a multicatolog of the neural network Gaussian classifier. The computational efficiency and the numerical stability of the training algorithm benefit from the well- established EM framework. The effectiveness of the proposed network architecture and its EM training algorithm are assessed by conducting two experiments.