CSVM and Its Application in the Chinese Theme Classification

摘要：

Support vector machine has been widely used in the classification issues. This paper proposed a new cascade support vector machine classification algorithm CSVM with AdaBoost algorithm framework and support vector machine SVM combination to deal with the problem of multiple classifiers, for the problem of consuming time in the multi-classification problems with support vector machines, this paper introduced the minimum enclosing ball (MKB) algorithm to extract the original sample data to shorten the training time for support vector machines;CSVM was applied in the Chinese theme classification, and the experimental results show that, CSVM algorithm has similar accuracy with AdaBoost algorithm, but the computation time is only 35% of the SVM algorithm1.

作者: WANG Guang QIU Yun-fei LI Hong-xia

作者单位: School of software LIAONING Technical University Huludao, China

会议类型: 国际会议

会议名称: 2010 International Conference on Optics,Photonics and Energy Engineering(2010光学、光子学与能源工程国际会议 OPEE 2010)

会议地点: 武汉

会议语种:英文

页码: 162-165

在线出版日期: 2010-05-10（万方平台首次上网日期，不代表论文的发表时间）