Active Learning for Classification: An Optimistic Approach

Abstract : In this paper, we propose to reformulate the active learning problem occurring in classification as a sequential decision making problem. We particularly focus on the problem of dynamically allocating a fixed budget of samples. This raises the problem of the trade off between exploration and exploitation which is traditionally addressed in the framework of the multi-armed bandits theory. Based on previous work on bandit theory applied to active learning for regression, we introduce four novel algorithms for solving the online allocation of the budget in a classification problem. Experiments on a generic classification problem demonstrate that these new algorithms compare positively to state-of-the-art methods.
Type de document :
Communication dans un congrès
IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2014), Dec 2014, Orlando, United States. IEEE, pp.1 - 8, 〈10.1109/ADPRL.2014.7010610〉
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https://hal.inria.fr/hal-01107508
Contributeur : Olivier Pietquin <>
Soumis le : mardi 20 janvier 2015 - 18:46:19
Dernière modification le : jeudi 5 avril 2018 - 12:30:11

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Timothé Collet, Olivier Pietquin. Active Learning for Classification: An Optimistic Approach. IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2014), Dec 2014, Orlando, United States. IEEE, pp.1 - 8, 〈10.1109/ADPRL.2014.7010610〉. 〈hal-01107508〉

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