摘要
贝叶斯网络结构学习是个NP难题。一种有效且准确性较高的学习算法是K2算法。但K2算法要确定结点次序,在无先验信息时受到很大限制。提出了一种启发式结构学习G算法,该算法以学习树扩展朴素贝叶斯TAN结构作为启发式信息,由该启发式信息生成结点次序,再用K2算法生成贝叶斯网络结构。实验结果表明,G算法可以解决无先验信息时确定结点次序的问题。所添加的弧比较简洁,网络结构比TAN结构更加合理。
The structure learning for Bayesian netwoks is NP - hard problem,K2 is one of efficacious and accurate algorithms. K2. confirms the order of nodes firstly. To a certain extent this limits in non - information. This paper purposes a new heuristic Bayesian networks structure learuing G algorithm. This algorithm uses TAN structure which learns as heuristic information, using K2 algorithm learning Bayesian netwoks structure. The experimental result shows that G algorithm can solve nodes order in non - information. Arcs is sententious, comparing TAN structure, it＇ s more reasonable.
出处
《计算机技术与发展》
2007年第8期61-63,共3页
Computer Technology and Development
基金
安徽省教育厅自然科学项目(2006KJ061B)
