|Table of Contents|

A Fast SVM Learning Algorithm(PDF)


Research Field:
Publishing date:


A Fast SVM Learning Algorithm
YangJingyu WeiXingguo SunHuaijiang
Department of Computer Science and Technology,NUST,Nanjing 210094
pattern recognition machine learning support vector machine learning algorithm
Support vector machine( SVM) and its learning algorithm for pattern classif icat ion are presented. Based on the analysis and comparison of the ex ist ing SVM t raining algorithms, especially SMO, a revised decomposit ion algorithm named GD is proposed. It balances w ell betw een the scale of the subquadratic programming problem and the efficiency and t imes of iteration. Experimental results show that it can substant ially reduce the t raining t ime of SVM w ith nonlinear kernels.


1 张学工. 关于统计学习理论与支持向量机[ J] . 自动化学报, 2000, 26( 1) : 32~ 42.
2 肖嵘, 王继成, 张福炎. 支持向量机理论综述[ J] . 计算机科学, 2000, 27( 3) : 1~ 3.
3 Perez-Cruz F, Navia-Vazquez A, Figueir as-Vidal A R, et al. Empir ical risk minimization for support vector classifiers[ J] . IEEE Transactions on Neural Netw orks, 2003, 14( 2) : 296~ 303.
4 Suykens J A K, van Gestel T, Vandewalle J, et al. A support vector machine formulation to PCA analysis and its kernel version[ J] . IEEE Transactions on Neural Networks, 2003, 14( 2) : 447~ 450.
5 魏兴国. 基于核方法的手写体数字识别研究[ D] . 南京: 南京理工大学计算机科学与技术系, 2003.
6 Cortes C, Vapnik V. Support vector networks[ J] . Machine Learning, 1995, 20( 2) : 273~ 297.
7 Platt J C. Fast training of SVM using sequential minimal optimization[ A] . Scholkopf B, Burges C J C, Smola A J. Advances in Kernel Methods ) Suppor t Vector Learning [ C] . Cambridge,MA: M IT Press, 1998. 185~ 208.
8 袁亚湘. 非线性规划数值方法[M] . 上海: 上海科学技术出版社, 1993.
9 Cr istianini N. Dynamically adapting kernels in suppor t v ector machines[ R] . London: Royal Holloway College, 1998.


Last Update: 2013-03-17