%0 Journal Article
%T Parameter Learning in Bayesian Network under Incomplete Evidence Input<br>不完备证据条件下的Bayesian网络参数学习
%A LIU Zhen
%A ZHOU Ming-Tian
%A <br>刘震
%A 周明天
%J 计算机科学
%D 2008
%I 
%X To Infer in a Bayesian network,parameter learning for a given network node is obviously necessary.But during the course of parameter learning,evidence loss would happen from time to time and therefore slow down the parameter convergence,influence the accuracy of parameter learning,and even cause no parameter convergence.Aiming at this question,this paper proposes a parameter model under evidence loss and deduce an EM updating algorithm which contains learning rate.Compared with the traditional algorithms,both of the converging performance analysis and simulation testing results show that new algorithm has much quicker convergence rate without degrading the accuracy of parameter estimation.New algorithm provides a feasible way to ensure a trusted and efficient Bayesian network parameter learning under the situation of evidence loss.
%K Bayesian网络
%K 证据丢失
%K EM(η)算法
%K 学习率
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=F9C3A0E3542C748AF77EEF8D2574A07B&yid=67289AFF6305E306&vid=6209D9E8050195F5&iid=CA4FD0336C81A37A&sid=73579BC9CFB2D787&eid=A58CF3BAE79427D0&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=15