An Updated Projection Twin Support Vector Machine for Classification

摘要：

　　Based on projection twin support vector machine(PTSVM)and its extensions,this paper describes an updated PTSVM(UPTSVM)for classification.Compared with existing PTSVMs,UPTSVM has its own advantages.First,similar to the standard support vector machine(SVM),UPTSVM maintains the consistency of the optimization problems in the linear and nonlinear case,which results in the nonlinear formulations can be directly turned into the linear ones.Nevertheless,the existing PTSVMs lose the consistency because of using empirical kernel to construct nonlinear formulations.Second,UPTSVM avoids the inverse of kernel matrixes in the course of solving dual problems,which indicates it can not only reduce computing time but also save storage space.Third,UPTSVM can be practically proved equivalent to the PTSVM with regularization(RPTSVM).Experimental results on lots of data sets show the virtue of the presented method.

作者: Xiaopeng Hua Sen Xu

作者单位: School of Information and Engineering,Yancheng Institute of Technology,224001 Yancheng,China

会议类型: 国际会议

会议名称: 2017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)(2017电子信息技术与计算机工程国际会议)(EITCE2017)

会议地点: 珠海

会议语种:英文

页码: 1-4

在线出版日期: 2017-09-23（万方平台首次上网日期，不代表论文的发表时间）