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

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.


68T05 Learning and adaptive systems in artificial intelligence
Full Text: Link