Fast Training of Support Vector Machines Using Error-Center-Based Optimization

作     者：L. Meng, Q. H. Wu Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, L69 3GJ, UK 

作者机构：Department of Electrical Engineering and Electronics The University of Liverpool Liverpool UK 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2005年第2卷第1期

页      面：6-12页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Support vector machines quadratic programming pattern classification machine learning 

摘      要：This paper presents a new algorithm for Support Vector Machine (SVM) training, which trains a machine based on the cluster centers of errors caused by the current machine. Experiments with various training sets show that the computation time of this new algorithm scales almost linear with training set size and thus may be applied to much larger training sets, in comparison to standard quadratic programming (QP) techniques.