Transductive Support Vector Machines

In contrast to learning a general prediction rule, V. Vapnik proposed the transductive learning setting where predictions are made only at a fixed number of known test points. This allows the learning algorithm to exploit the location of the test points, making it a particular type of semi-supervised learning problem. Transductive support vector machines (TSVMs) implement the idea of transductive learning by including test points in the computation of the margin. This chapter will provide some examples for why the margin on the test examples can provide useful prior information for learning, in particular for the problem of text classification. The resulting optimization problems, however, are difficult to solve. The chapter reviews exact and approximate optimization methods and discusses their properties. Finally, the chapter discusses connections to other related semi-supervised learning approaches like co-training and methods based on graph cuts, which can be seen as solving variants of the TSVM optimization problem.