A NEW FAST TRAINING ALGORITHM FOR SVM

摘要：

A fast SVM training algorithm is proposed in this paper. By integrating kernel caching, shrinking and using second order information, a fast Quadric Programming(QP) trainer is achieved. For traditional two-class SVM, the generalized error bound derived from Statistical Learning Theory(SLT) is computed and minimized for the selection of parameters, with the Zoutendijk(ZQP) idea and parallel method to speed up the process. For one-class SVM, a compression criterion is proposed to search the best kernel width automatically. Experiments demonstrate that the proposed method is significantly faster than LibSVM and requires less support vectors to achieve good classification accuracy.

关键词： Support vector machine statistical learning theory Gaussian kernel

作者: ZHI-JIE HE LIAN-WEN JIN

作者单位: School of Electronics and Information Engineering, South China University of Technology

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3451-3456

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）