Optimization techniques for semi-supervised support vector machines. (English) Zbl 1225.68158

Chapelle, Olivier; Sindhwani, Vikas; Keerthi, Sathiya S.

J. Mach. Learn. Res. 9, 203-233 (2008).

Summary: Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-supervised support vector machines (\(\text{S}^{3}\text{VMs}\)) 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 \(\text{S}^{3}\text{VMs}\). This paper reviews key ideas in this literature. The performance and behavior of various \(\text{S}^{3}\text{VM}\) algorithms is studied together, under a common experimental setting.

MSC:

68T05

Learning and adaptive systems in artificial intelligence

Keywords:

semi-supervised learning; support vector machines; non-convex optimization; transductive learning