AAAI Publications, Twenty-Second International Joint Conference on Artificial Intelligence

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Semi-Supervised Learning from a Translation Model Between Data Distributions
Henry Anaya-Sánchez, José Martínez-Sotoca, Adolfo Martínez-Usó

Last modified: 2011-06-28


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.

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