MULTIBOOST: a multi-purpose boosting package

Authors:
Djalel Benbouzid;Róbert Busa-Fekete;Norman Casagrande;François-David Collin;Balázs Kégl
Affiliations:
Linear Accelerator Laboratory, University of Paris-Sud, CNRS, Orsay, France;Linear Accelerator Laboratory, University of Paris-Sud, CNRS, Orsay, France;Wavii, Inc., London, United Kingdom;Linear Accelerator Laboratory, University of Paris-Sud, CNRS, Orsay, France;Linear Accelerator Laboratory, University of Paris-Sud, CNRS, Orsay, France
Venue:
The Journal of Machine Learning Research
Year:
2012

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Abstract

The MULTIBOOST package provides a fast C++ implementation of multi-class/multi-label/multitask boosting algorithms. It is based on ADABOOST.MH but it also implements popular cascade classifiers and FILTERBOOST. The package contains common multi-class base learners (stumps, trees, products, Haar filters). Further base learners and strong learners following the boosting paradigm can be easily implemented in a flexible framework.