Bayesian Networks Structure Based on the Mazimum Information Entropy and CBR

摘要：

Learning Bayesian Networks Structure from data often cost a lot of time because of the huge searching space, this article proposed a method of Bayesian Networks Structure based on the maximum information entropy and Case-based Reasoning (CBR), so that searching ends with complete picture because of only using maxi mutual information principle is avoided. We use this method in the Bayesian Networks structure, the result shows that the efficiency and the precision improved effectively.

关键词： Bayesian Network Mazimum Information Entropy Structure Learning Case-based Reasoning Case-based

作者: WAN Xinghuo TAN Yili GAO Yan LI Yan

作者单位: School of Science, Hebei Polytechnic University, Tangshan, P.R.China, 063009 Department of Mathematics and Information Science, Tangshan Teachers College, Tangshan, P.R.China, 0

会议类型: 国际会议

会议名称: 2008年国际应用统计学术研讨会(2008 International Institute of Applied Statistics Studies)

会议地点: 烟台

会议语种:英文

页码: 1-5

在线出版日期: 2008-08-14（万方平台首次上网日期，不代表论文的发表时间）