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.

• Type: Journal
• Year: 2008
• Where: JMLR
• Authors: Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
• Keywords: JMLR 2008, Margin Maximization Principle, Non-convex Optimization Problem, Support Vector Machines