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Online adaptive policies for ensemble classifiers

Christos Dimitrakakis (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers)) ; S. Bengio
Neurocomputing (0925-2312). Vol. 64 (2005), 1-4 SPEC. ISS., p. 211-221.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Boosting , Ensembles , Mixture of experts , Neural networks , Q-learning , Reinforcement learning , Supervised learning



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Denna post skapades 2013-12-18. Senast ändrad 2015-01-08.
CPL Pubid: 189779

 

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