产生式与判别式组合分类器学习算法  

A learning algorithm of a generative and discriminative combination classifier

作　　者：江雪莲[1] 石洪波[1] 

机构地区：[1]山西财经大学信息管理学院,山西太原030006

出　　处：《山东大学学报（理学版）》2010年第7期7-12,共6页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金资助项目(60873100);山西省自然科学基金资助项目(2009011017-4)

摘　　要：在AdaBoost集成方法的基础上,研究了一种产生式与判别式模型组合的方法。该算法在每轮中同时学习一个产生式分类器和一个判别式分类器,选择误差率较小的作为个体分类器,然后对所有个体分类器采用加权的方法得到最终分类器。实验结果表明,该方法在准确率和收敛速度上都具有很好的效果。Based on the AdaBoost ensemble framework,a learning algorithm of generative/discriminative combination classifier was proposed.In each round of the algorithm,a generative classifier and a discriminative classifier were learned,and the classifier with the smaller error rate was selected as the individual classifier,and then all the individual classifiers were combined by a weighted approach.Experimental results showed that this method was very good for accuracy and convergence speed.

关 键 词：产生式模型 判别式模型 集成分类器 个体分类器 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]