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Semi-Supervised Learning$
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Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien

Print publication date: 2006

Print ISBN-13: 9780262033589

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262033589.001.0001

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Transductive Support Vector Machines

Transductive Support Vector Machines

(p.104) (p.105) 6 Transductive Support Vector Machines
Semi-Supervised Learning

Joachims Thorsten

The MIT Press

This chapter discusses the transductive learning setting proposed by Vapnik where predictions are made only at a fixed number of known test points. Transductive support vector machines (TSVMs) implement the idea of transductive learning by including test points in the computation of the margin. This chapter provides 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 such as co-training and methods based on graph cuts, which can be seen as solving variants of the TSVM optimization problem.

Keywords:   transductive learning setting, Vapnik, transductive support vector machines, TSVMs, text classification, optimization problems, co-training, graph cuts

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