ARTIFICIAL FISH-SWARM ALGORITHM BASED PARAMETER LEARNING METHOD FOR BAYESIAN NETWORK

摘要：

The problem of learning Bayesian network parameter is known to be a hard problem.In this paper, a parameter learning method based on Artificial Fish-swarm Algorithm (AFSA) is presented for Bayesian networks with Noisy-Or and Noisy-And nodes.The learning approach is expatiated and the convergence is improved by adjusting the random moves speed of artificial fish.The experimental results show that this learning method is feasible and preferable.

关键词： Bayesian Network Parameter Learning Artificial fish-swarm Algorithm Noisy-Or Noisy-And

作者: Yan Wang Changming Zhang Dewen Wang

作者单位: School of Computer Science and Technology,North China Electric Power University Baoding 071000,P.R.China

会议类型: 国际会议

会议名称: 2009 International Symposium on Computer Science and Technology(2009 中国宁波国际计算机科学与技术学术大会)

会议地点: 宁波

会议语种:英文

页码: 492-495

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）