Abstract：In the model selection of influence diagrams(IDs), the problems of the data dependency, the computation complexity and nonprobability relation are discussed. Based on the structure decomposition of IDs, a PSEM algorithm is presented. A BP Neural Network is introduced by learning local utility function of each utility node, and the overfitting is avoided by inducing the threshold of weights. To reduce the data dependency, a new MDL scoring is presented which includes the prior knowledge of network structures. Based on SEM algorithm, PSEM algorithm induces the new MDL scoring, and separates parameters learning from structures scoring to improve the computation efficiency. Compared with SEM algorithm, the performances of both the computation complexity and the data dependency of PSEM algorithm are improved, and the model selection of the utility part is easy to achieve.
姚宏亮，张佑生，王浩，汪荣贵. 基于PSEM算法和BP神经网络的影响图模型选择*[J]. 模式识别与人工智能, 2007, 20(2): 185-190.
YAO HongLiang, ZHANG YouSheng , WANG Hao, Wang RongGui. A Model Selection Method of Influence Diagrams Based on PSEM Algorithm and BP Neural Network. , 2007, 20(2): 185-190.
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