Sparse Pinball Twin Parametric Margin Support Vector Machine

被引：0
|
作者：
Deepan, Urairat [1 ]
Kumam, Poom [1 ,2 ]
Chaipanya, Parin [1 ,2 ]
机构：
[1] King Mongkuts Univ Technol Thonburi KMUTT, Fac Sci, Dept Math, KMUTT Fixed Point Res Lab,Sci Lab Bldg,Fixed Poin, Room SCL 802,126 Pracha Uthit Rd, Bangkok 10140, Thailand
[2] King Mongkuts Univ Technol Thonburi KMUTT, Fac Sci, Theoret & Computat Sci Ctr, KMUTT Fixed Point Theory & Applicat Res Grp,Sci L, 126 Pracha Uthit Rd, Bangkok 10140, Thailand
来源：
THAI JOURNAL OF MATHEMATICS | 2021年 / 19卷 / 02期
关键词：
Epsilon insensitive loss; noise insensitivity; Twin Parametric Support Vector Machine (TSVM);
D O I：
暂无
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The main purpose of this paper is to construct a twin parametric margin support vector machine combined an epsilon-insensitive loss function for finding a pair of parametric margin hyperplanes that automatically adapts to the parametric noise with arbitrary shape to capture the data structure more accurately. We exhaustively test several UCI datasets demonstrates that our SPTPMSVM is noise insensitive, retains sparsity in most cases. Finally, we present the numerical experiment and compare our model with other models.
引用
收藏
页码：607 / 622
页数：16
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