Can AdaBoost.M1 learn incrementally? A comparison to learn++ under different combination rules

Hussein Syed Mohammed, James Leander, Matthew Marbach, Robi Polikar

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

    Abstract

    We had previously introduced Learn++, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn++ and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost's distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.

    Original languageEnglish (US)
    Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
    PublisherSpringer Verlag
    Pages254-263
    Number of pages10
    ISBN (Print)3540386254, 9783540386254
    StatePublished - Jan 1 2006
    Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
    Duration: Sep 10 2006Sep 14 2006

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4131 LNCS - I
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other16th International Conference on Artificial Neural Networks, ICANN 2006
    CountryGreece
    CityAthens
    Period9/10/069/14/06

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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