PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-Supervised Learning with Explicit Misclassification Modeling
Massih Amini and Patrick Gallinari
In: IJCAI 2003, 9-15 Aug 2003, Acapulco, Mexico.


This paper investigates a new approach for training discriminant classifiers when only a small set of labeled data is available together with a large set of unlabeled data. This algorithm optimizes the classification maximum likelihood of a set of labeled-unlabeled data, using a variant form of the Classification Expectation Maximum (CEM) algorithm. Its originality is that it makes use of both unlabeled data and of a probabilistic misclassification model for these data. The parameters of the label-error model are learned together with the classifier parameters. We demonstrate the effectiveness of the approach on four data-sets and show the advantages of this method over a previously developed semi-supervised algorithm which does not consider imperfections in the labeling process.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2642
Deposited By:Massih Amini
Deposited On:22 November 2006