Exponentiated Gradient Exploration for Active Learning

Computers

Djallel Bouneffouf¹ 

[1] Department of Computer Science, Télécom SudParis, UMR CNRS Samovar, 91011 Evry Cedex, France

关键词: active learning;    exploration and exploitation;    exponentiated gradient;   

DOI  :  10.3390/computers5010001

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2016 by the author; licensee MDPI, Basel, Switzerland.