%0 Journal Article
%T Selective SVM ensemble based on accelerating genetic algorithm<br>基于加速遗传算法的选择性支持向量机集成*
%A CHEN Tao
%A <br>陈涛
%J 计算机应用研究
%D 2011
%I 
%X This paper presented selective SVM ensemble based on accelerating genetic algorithm to improve the generalization ability of SVM.Produced many SVM by Bootstrap methods,established the fitness function based on negative correlation learning to improve generalization and high dissimilarity with others.Calculated the weighte of SVM by accelerating genetic algorithm, then ensembled those SVMs with weight larger than a given threshold value using weights average. Experiments results show that the algorithm is an effect ensemble method and improves the ensemble efficiency and generalization ability of SVM.
%K accelerating genetic algorithm(AGA)
%K the fitness function
%K negative correlation learning
%K support vector machine(SVM)
%K selective ensemble<br>加速遗传算法
%K 适应函数
%K 负相关学习
%K 支持向量机
%K 选择性集成
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=27B00B13FD7E3C136F5F54243BFFAD01&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=CA4FD0336C81A37A&sid=12DC19455C3A2FA8&eid=A8DE7703CC9E390F&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10