Chapter 1 OPTIMIZATION APPROACHES TO SEMI-SUPERVISED LEARNING

Abstract

We examine mathematical models for semi-supervised support vector machines (SVM). Given a training set of labeled data and a working set of unlabeled data, SVM constructs a support vector machine using both the training and working sets. We use SVM to solve the transductive inference problem posed by Vapnik. In transduction, the task is to estimate the… (More)

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