A Kernel Clustering Algorithm for Fast Training of Support Vector Machines  

作　　者：刘笑嶂 冯国灿 

机构地区：[1]School of Electronics and Information,Heyuan Polytechnic [2]Faculty of Mathematics and Computing,Sun Yat-sen University

出　　处：《Journal of Donghua University(English Edition)》2011年第1期53-56,共4页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China (No. 60975083);Key Grant Project,Ministry of Education,China(No. 104145)

摘　　要：A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy.A new algorithm named kernel bisecting k-means and sample removal（KBK-SR） is proposed as sampling preprocessing for support vector machine（SVM） training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy.

关 键 词：support vector machines(SVMs) sample reduction topdown hierarchical clustering kernel bisecting k-means 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]