ECAI-2002 Conference Paper

[full paper]

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.