Online adaptive policies for ensemble classifiers
Artikel i vetenskaplig tidskrift, 2005
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.
Q-learning
Mixture of experts
Ensembles
Neural networks
Boosting
Reinforcement learning
Supervised learning
Författare
Christos Dimitrakakis
Chalmers, Data- och informationsteknik, Datavetenskap
S. Bengio
Neurocomputing
0925-2312 (ISSN) 18728286 (eISSN)
Vol. 64 1-4 SPEC. ISS. 211-221Styrkeområden
Informations- och kommunikationsteknik
Ämneskategorier
Data- och informationsvetenskap
DOI
10.1016/j.neucom.2004.11.031