Optimization Techniques for Semi-Supervised Support Vector Machines

Chapelle, O; Sindhwani, V; Keerthi, SS

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Optimization Techniques for Semi-Supervised Support Vector Machines

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http://jmlr.csail.mit.edu/papers/volume9/chapelle08a/chapelle08a.pdf
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Chapelle, O., Sindhwani, V., & Keerthi, S. (2008). Optimization Techniques for Semi-Supervised Support Vector Machines. Journal of Machine Learning Research, 9, 203-233.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CA6D-1

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.