Exponentiated Gradient Exploration for Active Learning
AbstractActive 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
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Bouneffouf, D. Exponentiated Gradient Exploration for Active Learning. Computers 2016, 5, 1.
Bouneffouf D. Exponentiated Gradient Exploration for Active Learning. Computers. 2016; 5(1):1.Chicago/Turabian Style
Bouneffouf, Djallel. 2016. "Exponentiated Gradient Exploration for Active Learning." Computers 5, no. 1: 1.
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