Sparse boosting

Authors:
Zhen James Xiang;Peter J. Ramadge
Affiliations:
Dept. of Electrical Engineering, Princeton University, NJ, USA;Dept. of Electrical Engineering, Princeton University, NJ, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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BoostingTree: parallel selection of weak learners in boosting, with application to ranking

Machine Learning

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Abstract

We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost. The algorithm can be viewed as a coordinate descent method for the l1-regularized Adaboost exponential loss function.