文献类型：期刊文献

英文题名：nu-Nonparallel support vector machine for pattern classification

作者：Tian, Yingjie[1];Zhang, Qin[1];Liu, Dalian[2]

第一作者：Tian, Yingjie

通讯作者：Liu, DL[1]

机构：[1]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[2]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China

第一机构：Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China.|[1141788]北京联合大学基础教学部;[11417]北京联合大学;

年份：2014

卷号：25

期号：5

起止页码：1007-1020

外文期刊名：NEURAL COMPUTING & APPLICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000342204600004)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 11271361, 71331005), the CAS/SAFEA International Partnership Program for Creative Research Teams, Major International (Regional) Joint Research Project (No. 71110107026), and the Ministry of water resources' special funds for scientific research on public causes (No. 201301094).

语种：英文

外文关键词：Support vector machine; Twin support vector machines; Nonparallel; Structural risk minimization principle; Sparseness

摘要：In this paper, we propose a novel nonparallel hyperplane classifier, named nu-nonparallel support vector machine (nu-NPSVM), for binary classification. Based on our recently proposed method, i.e., nonparallel support vector machine (NPSVM), which has been proved superior to the twin support vector machines, nu-NPSVM is parameterized by the quantity nu to let ones effectively control the number of support vectors. By combining the nu-support vector classification and the nu-support vector regression together to construct the primal problems, nu-NPSVM inherits the advantages of nu-support vector machine so that enables us to eliminate one of the other free parameters of the NPSVM: the accuracy parameter epsilon and the regularization constant C. We describe the algorithm, give some theoretical results concerning the meaning and the choice of nu, and also report the experimental results on lots of data sets to show the effectiveness of our method.