A New Semidefinite Programming for Semi-supervised Support Vector Machines

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.

关键词： semi-supervised SVMs semidefinite programming machine learning

作者: Yi Chen Yanqin Bai

作者单位: Department of Mathematics, Shanghai University, Shanghai, 200444, China

会议类型: 国际会议

会议名称: The Third International Workshop on Applied Matriz Theory(第三届国际矩阵分析与应用会议)

会议地点: 杭州

会议语种:英文

页码: 350-353

在线出版日期: 2009-07-09（万方平台首次上网日期，不代表论文的发表时间）