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基于模型似然的超1依赖贝叶斯分类器集成方法*

, PP. 727-731

Keywords: 机器学习,数据挖掘,贝叶斯学习,朴素贝叶斯,集成学习

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Abstract:

平均1依赖贝叶斯分类器(AODE)是一种重要的贝叶斯学习方法,但由于其平等看待各个超1依赖贝叶斯分类器输出,可能对最终结果造成不好影响.本文将每个超1依赖贝叶斯分类器看作一个产生式模型,并通过模型似然度量超1依赖贝叶斯分类器的性能,进而提出基于模型似然的超1依赖贝叶斯分类器集成方法(LODE).与AODE相比,LODE仅增加较少计算量却显著提高分类性能.

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