Exponentiated Gradient Exploration for Active Learning

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 c...

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Bibliographic Details
Published in:Computers (Basel) Vol. 5; no. 1; p. 1
Main Author: Bouneffouf, Djallel
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2016
Subjects:
active learning
Algorithms
Boundaries
Exploration
exploration and exploitation
exponentiated gradient
Learning
Mathematical models
Optimization
Strategy
Tasks
ISSN:2073-431X, 2073-431X
Online Access:Get full text
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Description
Summary: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.
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers5010001