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

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.