Ramp Loss Linear Programming Nonparallel Support Vector Machine

被引：3

作者：

Liu, Dalian ^{[1
,2
]}

Chen, Dandan ^{[3
,4
]}

Shi, Yong ^{[1
,4
,5
,6
]}

Tian, Yingjie ^{[4
,5
]}

机构：

[1] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China

[2] Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China

[3] Univ Chinese Acad Sci, Coll Math Sci, Beijing 100049, Peoples R China

[4] Univ Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China

[5] Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China

[6] Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA

来源：

INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE 2016 (ICCS 2016) | 2016年 / 80卷

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

support vector machine; nonparallel; CCCP; linear programming; ramp loss;

D O I：

10.1016/j.procs.2016.05.432

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Motivated by the fact that the l(1)-penalty is piecewise linear, we proposed a ramp loss linear programming nonparallel support vector machine (ramp-LPNPSVM.), in which the l(1)-penalty is applied for the RNPSVM, for binary classification. Since the ramp loss has the piecewise linearity as well, ramp-LPNPSVM. is a piecewise linear minimization problem and a local minimum can be effectively found by the Concave Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm. Moreover, the l(1)-penalty can enhance the sparsity.

引用

页码：1745 / 1754

页数：10

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