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Optimism in Active Learning
Joint Authors
Collet, Timothé
Pietquin, Olivier
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-11-23
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Active learning is the problem of interactivelyconstructing the training set used in classificationin order to reduce its size.
It would ideallysuccessively add the instance-label pairthat decreases the classification error most.
However,the effect of the addition of a pair is notknown in advance.
It can still be estimatedwith the pairs already in the training set.
Theonline minimization of the classification errorinvolves a tradeoff between exploration andexploitation.
This is a common problem inmachine learning for which multiarmed bandit,using the approach of Optimism int the Face of Uncertainty, has proven very efficient these lastyears.
This paper introduces three algorithmsfor the active learning problem in classificationusing Optimism in the Face of Uncertainty.
Experiments lead on built-in problems and realworld datasets demonstrate that they comparepositively to state-of-the-art methods.
American Psychological Association (APA)
Collet, Timothé& Pietquin, Olivier. 2015. Optimism in Active Learning. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1057792
Modern Language Association (MLA)
Collet, Timothé& Pietquin, Olivier. Optimism in Active Learning. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-17.
https://search.emarefa.net/detail/BIM-1057792
American Medical Association (AMA)
Collet, Timothé& Pietquin, Olivier. Optimism in Active Learning. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1057792
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057792