Online coordinate boosting

Conference Paper

We present a new online boosting algorithm for updating the weights of a boosted classifier, which yields a closer approximation to the edges found by Freund and Schapire's AdaBoost algorithm than previous online boosting algorithms. We contribute a new way of deriving the online algorithm that ties together previous online boosting work. The online algorithm is derived by minimizing AdaBoost's loss as a single example is added to the training set. The equations show that the optimization is computationally expensive. However, a fast online approximation is possible. We compare approximation error to edges found by batch AdaBoost on synthetic datasets and generalization error on face datasets and the MNIST dataset. ©2009 IEEE.

Full Text

Duke Authors

Cited Authors

  • Pelossof, R; Jones, M; Vovsha, I; Rudin, C

Published Date

  • December 1, 2009

Published In

  • 2009 Ieee 12th International Conference on Computer Vision Workshops, Iccv Workshops 2009

Start / End Page

  • 1354 - 1361

International Standard Book Number 13 (ISBN-13)

  • 9781424444427

Digital Object Identifier (DOI)

  • 10.1109/ICCVW.2009.5457454

Citation Source

  • Scopus