PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online Adaptive Policies for Ensemble Classifiers
Christos Dimitrakakis and Samy Bengio
Neurocomputing 2004.

Abstract

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.

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EPrint Type:Article
Additional Information:This is an extended version of the ESANN conference paper.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:686
Deposited By:Christos Dimitrakakis
Deposited On:29 December 2004