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Online adaptive pol...
Abstract
Subject headings
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- Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, we attempt to train and combine the base classifiers using an adaptive policy. This policy is learnt through a Q-learning inspired technique. Its effectiveness for an essentially supervised task is demonstrated by experimental results on several UCI benchmark databases. © 2005 Elsevier B.V. All rights reserved.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Q-learning
- Mixture of experts
- Ensembles
- Neural networks
- Boosting
- Reinforcement learning
- Supervised learning
Publication and Content Type
- art (subject category)
- ref (subject category)
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