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arXiv · 1506.02535

Efficient Learning of Ensembles with QuadBoost

Louis Fortier-Dubois·François Laviolette·Mario Marchand·Louis-Emile Robitaille·Jean-Francis Roy
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Abstract

We first present a general risk bound for ensembles that depends on the Lp norm of the weighted combination of voters which can be selected from a continuous set. We then propose a boosting method, called QuadBoost, which is strongly supported by the general risk bound and has very simple rules for assigning the voters' weights. Moreover, QuadBoost exhibits a rate of decrease of its empirical error which is slightly faster than the one achieved by AdaBoost. The experimental results confirm the expectation of the theory that QuadBoost is a very efficient method for learning ensembles.

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Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Emile Robitaille, Jean-Francis Roy. 2015-06-08. Efficient Learning of Ensembles with QuadBoost. https://arxiv.org/abs/1506.02535

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