Research on New Reduction Strategy of SVM Used to Large-Scale Training Sample Set

摘要：

It has become a bottleneck to use Support Vector Machine (SVM) due to such problems as slow learning speed, large buffer memory requirement, low generalization performance and so on, which are caused by large-scale training sample set. Concerning these problems, this paper proposed a new reduction strategy for large-scale training sample set. First authors train an initial classifier with a small training set, which is randomly selected from the original samples, then cut the vector which is not Support Vector with the initial classifier to obtain a small reduction set. Training with this reduction set, final classifier is obtained. Experiments show that the learning strategy not only reduces the cost greatly but also obtains a classifier that has almost the same accuracy as the classifier obtained by training large set directly. In addition, speed of classification is greatly improved.

关键词： Support Vector Machine large-scale training sample reduction Support Vector

作者: Wang Anna Zhao Fengyun Li Yunlu Wang Jinbo

作者单位: College of Information Science and Engineering Northeastern University Shenyang, Liaoning, China College of Information Science and Engineering NortheasternUniversity Shenyang, Liaoning, China

会议类型: 国际会议

会议名称: 2011 International Conference on Electronics and Optoelectronics(2011电子学与光电子学国际会议 ICEOE 2011)

会议地点: 大连

会议语种:英文

页码: 5-8

在线出版日期: 2011-07-29（万方平台首次上网日期，不代表论文的发表时间）