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Semi-Supervised Learning from a Translation Model between Data Distributions

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1165-1169.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-199

Abstract

In this paper, we introduce a probabilistic classification model to address the task of semi-supervised learning. The major novelty of our proposal stems from measuring distributional relationships between the labeled and unlabeled data. This is achieved from a stochastic translation model between data distributions that is estimated from a mixture model. The proposed classifier is defined from the combination of both the translation model and a kernel logistic regression on labeled data. Experimental results obtained over synthetic and real-world data sets validate the usefulness of our proposal.

Henry Anaya-Sánchez, José Martínez Sotoca, and Adolfo Martínez Usó. 2011. Semi-Supervised Learning from a Translation Model between Data Distributions. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1165–1169. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-199
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