Semi-Supervised Learning with Explicit Misclassification Modeling
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 555-559.
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 labeledunlabeled 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 labelerror 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.
@inproceedings{ijcai2003p82,
title = {Semi-Supervised Learning with Explicit Misclassification Modeling},
author = {Amini, Massih-Reza and Gallinari, Patrick},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {555--559},
year = {2003},
url = {https://ijcai.org/proceedings/2003/82},
}
TY - CPAPER TI - Semi-Supervised Learning with Explicit Misclassification Modeling AU - Amini, Massih-Reza AU - Gallinari, Patrick T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 555 EP - 559 UR - https://ijcai.org/proceedings/2003/82 ER -Copied
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