%0 Journal Article
%T AN EFFICIENT EM TRAINING ALGORITHM FOR PROBABILITY MAPPING NETWORKS<br>一种概率映射网络的EM训练算法
%A Xiong Hanchun
%A He Qianhua
%A Li Haizhou
%A <br>熊汉春
%A 贺前华
%A 李海洲
%J 电子与信息学报
%D 1999
%I 
%X 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.
%K Probability mapping networks
%K EM algorithm
%K Bayes strategy
%K Mixture of Gaussian kerwl
%K Speaker recogonition<br>概率映射网络
%K EM算法
%K 贝叶斯策略
%K 高斯核混合
%K 说话人识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=25D89C1A001E48477FEEB9ABED2BC6DF&yid=B914830F5B1D1078&vid=659D3B06EBF534A7&iid=0B39A22176CE99FB&sid=A58CF3BAE79427D0&eid=7EBE588F611589FC&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=4