Prune the set of SV to Improve the Generalization Performance of SVM

摘要：

Initiated by that the quality of training data may affect the model selection, this paper presents a method to improve the prediction performance of SVM through pruning the set of SV. That is, using a global comparable noise measure based on neighbor distribution information to identify noisy SVs, and weaken their role in training. The difference of this method from traditional one is that it need not to process noise for every instance in training set, and but only for those in SVs. The experiment result shows that when top noisy SVs are weakened the prediction performance of SVM is better for most categories.

作者: Ziqiang Li Mingtian Zhou HaiBo Pu

作者单位: School of Computer Science and Engineering, University of Electronic Science and Technology of China School of Computer Science and Engineering, University of Electronic Science and Technology of China School of information and Engineering, Sichuan Agricultural University， Yaan , P.R.China

会议类型: 国际会议

会议名称: 2010 International Conference on Communications,Circuits and Systems(2010年通信、电路与系统国际会议)

会议地点: 成都

会议语种:英文

页码: 486-490

在线出版日期: 2010-06-28（万方平台首次上网日期，不代表论文的发表时间）