Online Coordinate Boosting

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dc.creatorPelossof, Raphael
dc.creatorJones, Michael
dc.creatorVovsha, Ilia
dc.creatorRudin, Cynthia
dc.date2008-10-24
dc.date.accessioned2026-07-07T10:13:13Z
dc.date.available2026-07-07T10:13:13Z
dc.descriptionWe present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire's AdaBoost algorithm than previous online boosting algorithms. We also contribute a new way of deriving the online algorithm that ties together previous online boosting work. We assume that the weak hypotheses were selected beforehand, and only their weights are updated during online boosting. The update rule is derived by minimizing AdaBoost's loss when viewed in an incremental form. The equations show that optimization is computationally expensive. However, a fast online approximation is possible. We compare approximation error to batch AdaBoost on synthetic datasets and generalization error on face datasets and the MNIST dataset.
dc.description9 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/0810.4553
dc.identifierhttp://arxiv.org/abs/0810.4553
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/172454
dc.subjectMachine Learning
dc.titleOnline Coordinate Boosting
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