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Abstract: This paper investigates the problem of augmenting labeled data with unlabeled data to improve classification accuracy. This is significant for many applications such as image classification where obtaining classification labels is expensive, while large unlabeled examples are easily available. We investigate an Expectation Maximization (EM) algorithm for learning from labeled and unlabeled data. The reason why unlabeled data boosts learning accuracy is because it provides the information about... (Update)
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BibTeX entry: (Update)
@misc{ wu-bem,
author = "Xintao Wu and Jianping Fan and Kalpathi R. Subramanian",
title = "B-EM: A Classifier Incorporating Bootstrap with EM Approach for Data Mining",
url = "citeseer.ist.psu.edu/707980.html" }
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