Approximate Minimum Enclosing Ball Algorithm with Smaller Core Sets for Binary Support Vector Machine

摘要：

Core Vector Machine (CVM) is a promising technique for scaling up a binary Support Vector Machine (SVM) to handle large data sets with the utilization of approximate Minimum Enclosing Ball (MEB) algorithm. However, the experimental results in implementation show that there always exists some redundancy in the final core set to determine the final decision function. We propose an approximate MEB algorithm in this paper to decrease the redundant core vectors as much as possible. The simulations on synthetic data sets demonstrate the competitive performances on training time, core vectors number and training accuracy.

关键词： Support Vector Machine Minimum Enclosing Ball Approximate algorithm Core vector

作者: Yongqing Wang Yan Li Liang Chang

作者单位: Department of Computer Science and Applications, ZhengZhou Institute of Aeronautical Industry Manage Computer Center, ZhengZhou Institute of Aeronautical Industry Management, ZhengZhou 450015, China College of Information Science and Technology, Beijing Normal University, Beijing 100875, China Virt

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3404-3408

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）