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Semi-Supervised Learning from a Translation Model Between Data Distributions

2019-11-13 | |  64 |   49 |   0

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 realworld data sets validate the usefulness of our proposal.

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