Sample Reduction Based on Kernel Squared Mahalanobis Distance for Support Vector Machines

摘要：

This paper presents a sample reduction algorithm based on kernel squared Mahalanobis distance, as a sampling preprocessing for SVM training to improve the scalability. Experimental results show that, the proposed algorithm is effective for reducing training samples for nonlinear SVMs.

作者: Xiao-Lin Zou Xiao-Zhang Liu

作者单位: Faculty of Mathematics and Information Sciences Zhaoqing University Zhaoqing, China School of Electronics and Information Heyuan Polytechnic Heyuan, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 272-276

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）