佛山科学技术学院学报（自然科学版）Issue(4)：26-30,5.
基于双支持向量机的大样本分类算法
Large sample classification based on Dual-SVM
摘要
Abstract
The dual support vector machine algorithm is proposed to improve the learning efficiency for large-scale sample classification based on two-phase training processes. In the first step, K-means clustering is performed to original training data of each class, all clustering centers are extracted and made up as a reduced-size training set for the first SVM training, then some cluster centers obtained as support vectors are regarded to be located close to the hyperplane border. In the second step, original samples are contained in clusters for which the cluster centers are obtained to be close to the hyperplane border, these border samples are then used in the second SVM classifier. Experimental results show that the proposed method leads to much faster SVM training without reducing the classification accuracy.关键词
支持向量机/大规模分类/聚类/样本选取Key words
support vector machine/large-scale classification/clustering/sample selection分类
信息技术与安全科学引用本文复制引用
胡小生..基于双支持向量机的大样本分类算法[J].佛山科学技术学院学报（自然科学版）,2015,(4):26-30,5.基金项目
广东高校优秀青年创新人才培养计划资助项目（2013LYM_0097，2014KQNCX184）；佛山科学技术学院校级科研项目 （）
