计算机应用研究2016，Vol.33Issue(11)：3219-3222,3227,5.DOI:10.3969/j.issn.1001--3695.2016.11.005
基于 Boosting框架的非稀疏多核学习方法
Non-sparse multiple kernel learning method based on Boosting framework
摘要
Abstract
Focus on the problem that the traditional classifier ensemble learning methods always integrated a single optimal classifier into the strong one,and the others,which maybe be useful to the optimal,were discarded simply in every Boosting iteration.This paper proposed a non-sparse multiple kernel learning method based on Boosting framework.At every iteration, firstly,this method selected a subset from the training dataset,then trained an optimal individual classifier by regularized non-sparse multiple kernel learning method with this subset,which was obtained by optimizing the non-sparse combination of M basic kernels.It retained some good kernels and discarded the bad ones through imposing LP-norm constrain on combination coefficients,and leaded to a selective kernel fusion and reserved more useful feature information.Lastly,these individual clas-sifiers were combined into the strong one.The proposed method has the advantages of ensemble learning methods as well as that of regularized non-sparse multiple kernel learning methods.Experiments show that it gains higher classification accuracy with smaller number of iterations compared with other Boosting methods.关键词
集成学习/非稀疏多核学习/弱分类器/基本核Key words
ensemble learning/non-sparse multiple kernel learning/weak classifier/basic kernel分类
信息技术与安全科学引用本文复制引用
胡庆辉,李志远..基于 Boosting框架的非稀疏多核学习方法[J].计算机应用研究,2016,33(11):3219-3222,3227,5.基金项目
国家自然科学基金资助项目（11301106）；广西自然科学基金资助项目（2014GXNSFAA1183105，2016GXNSFAA380226）；广西高校科研项目 （）
