Optimization approaches for semi-supervised learning

被引：0

作者：

Yajima, Y ^{[1
]}

Hoshiba, T ^{[1
]}

机构：

[1] Tokyo Inst Technol, Dept Ind Engn & Management, Meguro Ku, Tokyo 1528552, Japan

来源：

ICMLA 2005: FOURTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present new approaches for semi-supervised learning based on the formulations of SVMs for the conventional supervised setting. The manifold structure of the data points given by the graph Laplacian can be taken into account in a efficient way. The proposed optimization problems fully enjoy the sparse structure of the graph Laplacian, which enables us to optimize the problems with a large number of data points in a practical amount of computational time. Some results of experiments showing the performance of our approaches are presented.

引用

页码：247 / 252

页数：6

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