Learning with ensemble of linear perceptrons

Pitoyo Hartono, Shuji Hashimoto

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

    3 Citations (Scopus)

    Abstract

    In this paper we introduce a model of ensemble of linear perceptrons. The objective of the ensemble is to automatically divide the feature space into several regions and assign one ensemble member into each region and training the member to develop an expertise within the region. Utilizing the proposed ensemble model, the learning difficulty of each member can be reduced, thus achieving faster learning while guaranteeing the overall performance.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages115-120
    Number of pages6
    Volume3697 LNCS
    Publication statusPublished - 2005
    Event15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005 - Warsaw
    Duration: 2005 Sep 112005 Sep 15

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3697 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005
    CityWarsaw
    Period05/9/1105/9/15

    ASJC Scopus subject areas

    • Computer Science(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Theoretical Computer Science

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  • Cite this

    Hartono, P., & Hashimoto, S. (2005). Learning with ensemble of linear perceptrons. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 115-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3697 LNCS).