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基于依赖分析的马尔科夫网络分类器学习与优化*

, PP. 485-490

Keywords: 分类器,马尔科夫网络,优化,弦图

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Abstract:

对可分解概率模式在0-1损失下证明马尔科夫网络分类器是最优分类器.针对目前建立马尔科夫网络分类器结构效率和可靠性低的问题,基于变量之间基本依赖关系、基本结构和依赖分析思想进行马尔科夫网络分类器结构学习来避免这些问题.并通过去除不相关和冗余属性变量的方法实现对马尔科夫网络分类器的优化,以提高抗噪声能力和预测能力.分别使用模拟和真实数据进行分类器分类准确性比较实验,实验结果显示优化后的马尔科夫网络分类器具有良好的分类准确性.

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