Semi-Supervised Learning with Explicit Misclassification Modeling

Massih-Reza Amini 1 Patrick Gallinari 1
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : 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 Maximization (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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Conference papers
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Submitted on : Tuesday, June 6, 2017 - 2:18:11 PM
Last modification on : Thursday, March 21, 2019 - 2:16:06 PM


  • HAL Id : hal-01533409, version 1


Massih-Reza Amini, Patrick Gallinari. Semi-Supervised Learning with Explicit Misclassification Modeling. IJCAI 2003 - 18th International Joint Conference on Artificial Intelligence, Aug 2003, Acapulco, Mexico. pp.555-560. ⟨hal-01533409⟩



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