The Asymptotics of Semi-Supervised Learning in Discriminative Probabilistic Models
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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