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JMLR
2008

Optimization Techniques for Semi-Supervised Support Vector Machines

9 years 3 months ago
Optimization Techniques for Semi-Supervised Support Vector Machines
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S3VMs. This paper reviews key ideas in this literature. The performance and behavior of various S3VM algorithms is studied together, under a common experimental setting.
Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keer
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where JMLR
Authors Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
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