%0 Journal Article
%T Learning Bayesian Network Structure from Small Data Set<br>小数据集的贝叶斯网络结构学习
%A WANG Shuang-Cheng LENG Cui-Ping LI Xiao-Lin School of Mathematics
%A Information
%A Shanghai Lixin University of Commerce
%A Shanghai Opening Economy
%A Trade Research Center
%A Shanghai School of Business
%A Nanjing University
%A Nanjing
%A <br>王双成
%A 冷翠平
%A 李小琳
%J 自动化学报
%D 2009
%I 
%X It is incredible to learn Bayesian network structure directly from small data set. For improving the reliability, many methods of extending small data set have been developed, but the revision of extended data is neglected. In this paper, extending small data set is combined with revising extended data to upswing the data reliability. A directed tree is built from the small data set and variables are sorted according to it. On the basis of the variable order, a Bayesian network structure can be established based on the local search and scoring method. This method dose not need the prior knowledge of the variable order, but the partial order information of expert can be used properly. Experimental results show that this method can effectively learn Bayesian network structure from a small data set.
%K Bayesian network
%K small data set
%K structure learning
%K maximal likelihood tree
%K Gibbs sampling<br>贝叶斯网络
%K 小数据集
%K 结构学习
%K 最大似然树
%K 吉布斯抽样
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=E1822F20EF00F4B218736679CD62BF8B&yid=DE12191FBD62783C&vid=6209D9E8050195F5&iid=5D311CA918CA9A03&sid=EE05CC1F800E4629&eid=A50445FB05D4B1A0&journal_id=0254-4156&journal_name=自动化学报&referenced_num=3&reference_num=0