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：
中国国家自然科学基金; 北京市自然科学基金;
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
收藏
页码：1745 / 1754
页数：10
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