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2008 JMLR JMLR 2008

Optimization Techniques for Semi-Supervised Support Vector Machines

Abstract

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 S3VMs algorithms is studied together, under a common experimental setting. [abs] [ pdf ][ bib ] © JMLR 2008. (edit, beta)

🐣 Hot Topic Early Bird — semi-supervised learning
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Authors

Olivier Chapelle , Vikas Sindhwani , Sathiya S. Keerthi

Topics

Machine Learning > Learning Types > Semi-Supervised Learning

Keywords

semi-supervised learning non-convex optimization label propagation margin maximization support vector machine
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