1. Optimization Techniques for Semi-Supervised Support Vector Machines.
- Author
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Chapelle, Olivier, Sindhwani, Vikas, and Keerthi, Sathiya S.
- Subjects
- *
MACHINE learning , *MATHEMATICAL optimization , *ALGORITHMS , *MATHEMATICAL analysis , *MATHEMATICS - 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 S³VM algorithms is studied together, under a common experimental setting. [ABSTRACT FROM AUTHOR]
- Published
- 2008