A family of online boosting algorithms

Boris Babenko, Ming Hsuan Yang, Serge Belongie

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

17 Citations (Scopus)

Abstract

Boosting has become a powerful and useful tool in the machine learning and computer vision communities in recent years, and many interesting boosting algorithms have been developed to solve various challenging problems. In particular, Friedman proposed a flexible framework called gradient boosting, which has been used to derive boosting procedures for regression, multiple instance learning, semi-supervised learning, etc. Recently some attention has been given to online boosting (where the examples become available one at a time). In this paper we develop a boosting framework that can be used to derive online boosting algorithms for various cost functions. Within this framework, we derive online boosting algorithms for Logistic Regression, Least Squares Regression, and Multiple Instance Learning. We present promising results on a wide range of data sets.

Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages1346-1353
Number of pages8
DOIs
Publication statusPublished - 2009 Dec 1
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: 2009 Sep 272009 Oct 4

Publication series

Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Conference

Conference2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
CountryJapan
CityKyoto
Period09/9/2709/10/4

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Babenko, B., Yang, M. H., & Belongie, S. (2009). A family of online boosting algorithms. In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 (pp. 1346-1353). [5457453] (2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009). https://doi.org/10.1109/ICCVW.2009.5457453