Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33645
Title: Totally-corrective boosting using continuous-valued weak learners
Authors: Sun, C
Zhao, S
Hu, J
Lam, KM 
Keywords: Boosting
Column generation
Gradient
Totally corrective
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 25-30 March 2012, Kyoto, p. 2049-2052 How to cite?
Abstract: The Boosting algorithm has two main variants: the gradient Boosting and the totally-corrective column-generation Boosting. Recently, the latter has received increasing attention since it exhibits a better convergence property, thus resulting in more efficient strong learners. In this work, we point out that the totally-corrective column-generation Boosting is equivalent to the gradient-descent method for the gradient Boosting in the weak-learner selection criterion, but uses additional totally-corrective updates for the weak-learner weights. Therefore, other techniques for the gradient Boosting that produce continuous-valued weak learners, e.g. step-wise direct minimization and Newtons method, may also be used in combination with the totally-corrective procedure. In this work we take the well known AdaBoost algorithm as an example, and show that employing the continuous-valued weak learners improves the performance when used with the totally-corrective weak-learner weight update.
URI: http://hdl.handle.net/10397/33645
ISBN: 978-1-4673-0045-2
978-1-4673-0044-5 (E-ISBN)
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2012.6288312
Appears in Collections:Conference Paper

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