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 (S 3 VMs) 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 S 3 VMs. This paper reviews key ideas in this literature. The performance and behavior of various S 3 VMs algorithms is studied together, under a common experimental setting. [abs] [ pdf ][ bib ] &copy JMLR 2008. ( edit, beta )

Authors

• Olivier Chapelle
• Vikas Sindhwani
• Sathiya S. Keerthi

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