文献类型：会议论文

英文题名：Nonparallel Hyperplanes Support Vector Machine for Multi-class Classification

作者：Ju, Xuchan[1,2];Tian, Yingjie[2,3];Liu, Dalian[4];Qi, Zhiquan[2,3]

第一作者：Ju, Xuchan

通讯作者：Tian, YJ[1]

机构：[1]Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China;[2]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing, Peoples R China;[3]Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China;[4]Beijing Union Univ, Dept Basic Course Teaching, Beijing, Peoples R China

第一机构：Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing, Peoples R China.

会议论文集：15th Annual International Conference on Computational Science (ICCS)

会议日期：JUN 01-03, 2015

会议地点：Reykjavik Univ, Reykjavik, ICELAND

主办单位：Reykjavik Univ

语种：英文

外文关键词：multi-class classification; support vector machine; nonparallel; quadratic programming; kernel function

摘要：In this paper, we proposed a nonparallel hyperplanes classifier for multi-class classification, termed as NHCMC. This method inherits the idea of multiple birth support vector machine( MBSVM), that is the "max" decision criterion instead of the "min" one, but it has the incomparable advantages than MBSVM. First, the optimization problems in NHCMC can be solved efficiently by sequential minimization optimization (SMO) without needing to compute the large inverses matrices before training as SVMs usually do; Second, kernel trick can be applied directly to NHCMC, which is superior to existing MBSVM. Experimental results on lots of data sets show the efficiency of our method in multi-class classification accuracy.