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基于PSEM算法和BP神经网络的影响图模型选择*

, PP. 185-190

Keywords: 影响图(IDs),结构期望最大值(SEM)算法,后向神经网络,最小描述长度(MDL)评分

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Abstract:

影响图模型选择中存在数据依赖性、计算复杂性和非概率关系问题.通过对影响图结构进行分解,提出PSEM算法对影响图的概率结构部分进行模型选择.给出一种BP神经网络,通过对局部效用函数的学习实现效用结构部分的模型选择,并引入权重阈值来避免过拟合.PSEM算法是在SEM算法中引入一种融合先验知识的MDL评分标准来降低传统MDL评分对数据的依赖性,并通过将参数学习和结构评分分开计算提高计算效率.算法比较的结果显示PSEM比标准SEM的时间性能好、对数据依赖性小,且效用部分的结构选择易于实现.

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