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Computers 2016, 5(1), 1; doi:10.3390/computers5010001

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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Abstract

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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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

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