计算机工程与应用Issue(15)：107-112,6.DOI:10.3778/j.issn.1002-8331.1307-0003
基于密度核估计的贝叶斯网络结构学习算法
Structure learning algorithm for Bayesian network based on probability density kernel estimation
摘要
Abstract
Structure learning algorithms for a Bayesian network mainly include hill-climbing algorithm, K2 algorithm and so on. However, these algorithms require large sample data sets. For the small sample sets in practical problems, this paper introduces the probability density kernel estimation method to achieve the expansion of the original sample set, and then uses the K2 algorithm for a Bayesian network structure learning. By optimizing the kernel function and window width, it achieves the effective expansion of the original sample set based on probability density kernel estimation;it confirms the variable order based on mutual information, and then establishes a Bayesian structure learning algorithm based on a small sample set. Simulation results show that the algorithm is effective and practical.关键词
贝叶斯网络/小样本结构学习/K2算法Key words
Bayesian network/structure learning based on small sample set/K2分类
信息技术与安全科学引用本文复制引用
韩绍金,李建勋..基于密度核估计的贝叶斯网络结构学习算法[J].计算机工程与应用,2014,(15):107-112,6.基金项目
国家重点基础研究发展规划（973）（No.2009CB824900）；国家自然科学基金（No.61175008，No.60935001）；航天支撑基金（No.2011-HT-SHJD002）。 （）
