Totally-corrective multi-class boosting

Zhihui Hao*, Chunhua Shen, Nick Barnes, Bo Wang

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)


    We proffer totally-corrective multi-class boosting algorithms in this work. First, we discuss the methods that extend two-class boosting to multi-class case by studying two existing boosting algorithms: AdaBoost.MO and SAMME, and formulate convex optimization problems that minimize their regularized cost functions. Then we propose a column-generation based totally-corrective framework for multi-class boosting learning by looking at the Lagrange dual problems. Experimental results on UCI datasets show that the new algorithms have comparable generalization capability but converge much faster than their counterparts. Experiments on MNIST handwriting digit classification also demonstrate the effectiveness of the proposed algorithms.

    Original languageEnglish
    Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
    Number of pages12
    EditionPART 4
    Publication statusPublished - 2011
    Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
    Duration: 8 Nov 201012 Nov 2010

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 4
    Volume6495 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference10th Asian Conference on Computer Vision, ACCV 2010
    Country/TerritoryNew Zealand

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