On cone optimization approaches for semi-supervised support vector machines(S3VM)

被引：0
|
作者：
Ahmed, Faizan [1 ]
Iqbal, Muhamamd Faisal [2 ]
Rafiq, Ayesha [2 ]
机构：
[1] Saxion Univ Appl Sci, Ambient Intelligence Grp, Enschede, Netherlands
[2] Inst Space Technol, Dept Appl Math & Stat, Islamabad, Pakistan
来源：
2019 2ND INTERNATIONAL CONFERENCE ON COMPUTING, MATHEMATICS AND ENGINEERING TECHNOLOGIES (ICOMET) | 2019年
关键词：
semi-supervised learning; cone programming; copositive programming; mixed integer quadratic program; MATRIX;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, a review of cone programming formulation of soft margin semi-supervised support vector machines is provided. The (SVM)-V-3 is known to be NP-hard, thus their cone programming reformulation remains NP-hard. However, the reformulation converts the problem into a convex optimization problem. The formulations can be classified into semidefinite programming reformulation and copositive reformulation. We have collected several semi-definite and copositive programming reformulations. The relations between these reformulations are also discussed.
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页数：6
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