Support Vectors Pre-extracting for Support Vector Machine Based on K Nearest Neighbour Method

摘要：

Support vector machine,a universal method for learning from data,gains its development based on statistical learning theory.It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition.Only a part of samples or support vectors (SVs) plays an important role in the final decision function.But SVs could not be obtained in advance until a quadratic programming is performed.In this paper,we use K -nearest neighbour method to extract a boundary vector set which may contain SVs.The number of the boundary set is smaller than the whole training set.Consequently it reduces the training samples,speeds up the training of support vector machine.

关键词： support vector machine K nearest neighbour pre- extracting.

作者: Li Zhang Ning Ye Weida Zhou Licheng Jiao

作者单位: Institute of Intelligent Information Processing &Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education University of Xidian Xi an 710071 ,Shaanxi Province,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 1353-1358

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）