Research In SVM Sample Optimizes of ISODATA Algorithm

摘要：

　　Support Vector Machine is widely used in data classification,but in the case of more training samples,the training time is longer.To solve this problem,use the ISODATA clustering algorithm to cluster samples to obtain the new cluster center,together with high similarity to the error for the sample to form a new cluster of training samples,training support vector machines.So that a solution of high similarity to repeat the training samples of similar problems,while focusing on the easily lead to wrong classification of the training samples.The support vector machine classification accuracy can be improved,and also reduces the training time,to make it more convenient for engineering application.

关键词： iterative self organizing data analysis support vector machines sample clustering classification

作者: Zhenjiang Zhao Wei Gao Huaizhong Wang Kefei Zhang

作者单位: College of Computer Science and Technology, Shenyang University of Chemical Technology,Shenvang Liaoning, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1507-1511

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）