Publication

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio
2005
Journal Articles

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.

Official source

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Samy Bengio
2005
Journal Articles

Abstract

Official source

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