%0 Journal Article
%T The Hybrid Learning Algorithm Which is Based on em Algorithm and can Globally Converge with Probability 1<br>基于em算法且能以概率1全局收敛的混合学习算法
%A WANG Shi-tong
%A <br>王士同
%J 软件学报
%D 1998
%I 
%X In this paper, the drawback is pointed out that the learning algorithm em of random neural network sometimes converges to local minimum. A new hybrid learning algorithm HRem, which combines algorithm em and the random optimization algorithm presented by Dr. Solis and Wets, is presented for 3-layer random perception. It is theoretically proved that algorithm HRem can globally converge to the minimum of Kullback-Leibler difference measure. This theoretical result has important significances for further research on algorithm em.
%K Random neural networks
%K em learning algorithm
%K random optimization algorithm<br>随机神经网络，em学习算法，随机优化算法.
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=3E601D70A4BDFBD1&yid=8CAA3A429E3EA654&vid=9CF7A0430CBB2DFD&iid=B31275AF3241DB2D&sid=A6683C8C0EB9BCA7&eid=30F3EEEA29E34EE7&journal_id=1000-9825&journal_name=软件学报&referenced_num=0&reference_num=5