15th European Conference on Artificial Intelligence |
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July 21-26 2002
Lyon France |
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Semi
Supervised Logistic Regression
Massih-Reza Amini, Patrick Gallinari
Semi-supervised
learning has recently emerged as a new paradigm in the machine learning
community. It aims at exploiting simultaneously labeled and unlabeled data for
classification. We introduce here a new semi-supervised algorithm. Its
originality is that it relies on a discriminative approach to semi-supervised
learning rather than a generative approach, as it is usually the case. We
present in details this algorithm for a logistic classifier and show that it
can be interpreted as an instance of the Classification Expectation
Maximization algorithm. We also provide empirical results on two data sets for
sentence classifcation tasks and analyze the behavior of our methods.
Keywords:
Machine Learning, Semi-Supervised Learning.
Citation: Massih-Reza Amini, Patrick
Gallinari: Semi Supervised Logistic Regression. In F. van Harmelen (ed.):
ECAI2002, Proceedings of the 15th European Conference on Artificial
Intelligence, IOS Press, Amsterdam, 2002, pp.390-394.
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ECAI-2002 is organised by the European Coordinating Committee for
Artificial Intelligence (ECCAI) and
hosted by the Université Claude Bernard and INSA,
Lyon, on behalf of Association Française pour l'Intelligence Artificielle.