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Computers 2016, 5(1), 1;

Exponentiated Gradient Exploration for Active Learning

Department of Computer Science, Télécom SudParis, UMR CNRS Samovar, 91011 Evry Cedex, France
Academic Editor: Pedro Alonso Jordá
Received: 24 November 2015 / Revised: 31 December 2015 / Accepted: 5 January 2016 / Published: 8 January 2016
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Active learning strategies respond to the costly labeling task in a supervised classification by selecting the most useful unlabeled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named exponentiated gradient (EG)-active that can improve any active learning algorithm by an optimal random exploration. Experimental results show a statistically-significant and appreciable improvement in the performance of our new approach over the existing active feedback methods. View Full-Text
Keywords: active learning; exploration and exploitation; exponentiated gradient active learning; exploration and exploitation; exponentiated gradient

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Bouneffouf, D. Exponentiated Gradient Exploration for Active Learning. Computers 2016, 5, 1.

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