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基于预测能力的贝叶斯网络结构学习

Learning Bayesian networks structure based on prediction ability

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【作者】 王辉张剑飞王双成

【Author】 WANG Hui~1,ZHANG Jian-fei~(2,1),WANG Shuang-cheng~3 (1.Institute of Computer,Northeast Normal University,Changchun 130024,China; 2.Information Science and Electrical Engineering School,Qiqihar University,Qiqihar 161006,China; 3.Institute of Computer Science and Technology,Jilin University,Changchun 130025,China)

【机构】 东北师范大学计算机学院齐齐哈尔大学信息科学与电气工程学院吉林大学计算机科学与技术学院 吉林长春130024黑龙江齐齐哈尔161006东北师范大学计算机学院吉林长春130024吉林长春130025

【摘要】 给出了变量之间预测能力的概念及估计方法,证明了预测能力就是预测正确率.在此基础上建立了基于预测能力的贝叶斯网络结构学习方法,并使用模拟数据进行了实验.实验结果显示该算法能够有效地进行贝叶斯网络结构学习.

【Abstract】 In the paper,the concept and estimating methods of the prediction ability are given.At the same time,it is proven that the prediction ability is the accurate of the prediction.The method of Bayesian networks structure learning based on prediction ability is developed.This method is made up of two parts:(1)setting up elementary Bayesian networks structure according to the absolute prediction ability;(2)regulating elementary Bayesian networks structure according to the conditional prediction ability and checking the loop.The experiment is made by simulation and the results are shown in the way of contrast,experimental results show that Bayesian networks structure can be learned by this method effectively.

【基金】 吉林省自然科学基金资助项目(20030517-1)
  • 【文献出处】 东北师大学报(自然科学版) ,Journal of Northeast Normal University (Natural Science Edition) , 编辑部邮箱 ,2005年01期
  • 【分类号】TP181
  • 【被引频次】20
  • 【下载频次】540
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