Optimism in Active Learning

Abstract : Active learning is the problem of interactively constructing the training set used in classification in order to reduce its size. It would ideally successively add the instance-label pair that decreases the classification error most. However, the effect of the addition of a pair is not known in advance. It can still be estimated with the pairs already in the training set. The online minimization of the classification error involves a tradeoff between exploration and exploitation. This is a common problem in machine learning for which multiarmed bandit, using the approach of Optimism int the Face of Uncertainty, has proven very efficient these last years. This paper introduces three algorithms for the active learning problem in classification using Optimism in the Face of Uncertainty. Experiments lead on built-in problems and real world datasets demonstrate that they compare positively to state-of-the-art methods.
Type de document :
Article dans une revue
Computational Intelligence and Neuroscience, Hindawi Publishing Corporation, 2015
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https://hal.inria.fr/hal-01225798
Contributeur : Olivier Pietquin <>
Soumis le : vendredi 6 novembre 2015 - 17:38:44
Dernière modification le : mardi 3 juillet 2018 - 11:21:18

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  • HAL Id : hal-01225798, version 1

Citation

Timothé Collet, Olivier Pietquin. Optimism in Active Learning. Computational Intelligence and Neuroscience, Hindawi Publishing Corporation, 2015. 〈hal-01225798〉

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