The Asymptotics of Semi-Supervised Learning in Discriminative Probabilistic Models

author: Olivier Cappé, +LTCI, TELECOM ParisTech and CNRS
published: Aug. 4, 2008,   recorded: July 2008,   views: 3889


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Semi-supervised learning aims at taking advantage of unlabeled data to improve the efficiency of supervised learning procedures. For discriminative models however, this is a challenging task. In this contribution, we introduce an original methodology for using unlabeled data through the design of a simple semi-supervised objective function. We prove that the corresponding semi-supervised estimator is asymptotically optimal. The practical consequences of this result are discussed for the case of the logistic regression model.

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