A New Semidefinite Programming for Semi-supervised Support Vector Machines

被引：0
|
作者：
Chen, Yi [1 ]
Bai, Yanqin [1 ]
机构：
[1] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China
来源：
PROCEEDINGS OF THE THIRD INTERNATIONAL WORKSHOP ON MATRIX ANALYSIS AND APPPLICATIONS, VOL 1 | 2009年
关键词：
semi-supervised SVMs; semidefinite programming; machine learning; OPTIMIZATION;
D O I：
暂无
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The problem of semi-supervised SVMs, which constructs a SVM using both the training data and unlabeled data has been formulated as an integer optimization problem. In this paper, we present a semidefinite programming formation for the problem of semi-supervised SVMs by using the approach based on the convex relaxation. The aim is to use the efficient interior point algorithms to solve SDP model of the problem of semi-supervised SVMs and obtain an approximation of the optimal labeling.
引用
收藏
页码：65 / 68
页数：4
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