W-boost and its application to Web image classification

Jingrui He, Mingjing Li, Hong Jiang Zhang, Changshui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

When training data is not sufficient, boosting algorithms tend to overfit as more weak learners are combined to form a strong classifier. In this paper, we propose a new variant of RealBoost, called W-Boost, which is based on a novel weight update scheme and uses changeable bin number to estimate marginal distributions in weak learner design. This new boosting procedure results in both fast convergence rate and small generalization error. Experimental results on synthetic data and web image classification demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages148-151
Number of pages4
DOIs
StatePublished - Dec 17 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: Aug 23 2004Aug 26 2004

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Other

OtherProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
CountryUnited Kingdom
CityCambridge
Period8/23/048/26/04

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    He, J., Li, M., Zhang, H. J., & Zhang, C. (2004). W-boost and its application to Web image classification. In J. Kittler, M. Petrou, & M. Nixon (Eds.), Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 (pp. 148-151). (Proceedings - International Conference on Pattern Recognition; Vol. 1). https://doi.org/10.1109/ICPR.2004.1334029