[1]张连文,郭海鹏. 贝叶斯网引论[M]. 北京:科学出版社, 2006.
[2]Cooper G F, Herskovits E. A Bayesian method for the induction of probabilistic networks from data[J]. Machine Learning, 1992,9(4):309-347.
[3]Bouckaert R R. Properties of Bayesian belief network learning algorithms[C]// Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence. 1994:102-109.
[4]Heckerman D, Geiger D, Chickering D M. Learning Bayesian networks: The combination of knowledge and statistical data[J]. Machine Learning, 1995,20(3):197-243.
[5]刘乐乐,田卫东. 基于属性互信息熵的量化关联规则挖掘[J]. 计算机工程, 2009,35(14):38-40.
[6]Sprites P, Glymour C, Scheines R. Causality from probability[C]// Evolving Knowledge in Natural and Artificial Intelligence. 1990:181-199.
[7]Cheng Jie, Greiner R, Kelly J, et al. Learning Bayesian networks from data: An efficient information-theory based approach[J]. Artificial Intelligence, 2002,137(1-2):43-90.
[8]刘峰. 贝叶斯网络结构学习算法研究[D]. 北京:北京邮电大学, 2008.
[9]朱明敏,刘三阳,汪春峰. 基于先验节点序学习贝叶斯网络结构的优化方法[J]. 自动化学报, 2011,37(12):1514-1519.
[10]Peng Hanchuan, Long Fuhui, Ding C. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(8):1226-1238.
[11]陈坤. 基于MRMR的贝叶斯网络结构学习算法研究[D]. 苏州:苏州大学, 2013.
[12]张平,刘三阳,朱明敏. 基于人工蜂群算法的贝叶斯网络结构学习[J]. 智能系统学报, 2014(3):325-329.
[13]Heckerman D. Bayesian networks for data mining[J]. Data Mining and Knowledge Discovery, 1997,1(1):79-119.
[14]高晓光,赵欢欢,任佳. 基于蚁群优化的贝叶斯网络学习[J]. 系统工程与电子技术, 2010,32(7):1509-1512.
[15]Tsamardinos I, Brown L E, Aliferis C F. The max-min hill-climbing Bayesian network structure learning algorithm[J]. Machine Learning, 2006,65(1):31-78.
[16]王双成,唐海燕,刘喜华. 用于离散变量因果分析的贝叶斯网络学习[J]. 系统工程学报, 2008,23(5):596-602. |