Learning-data selection mechanism through neural networks ensemble

Pitoyo Hartono, Shuji Hashimoto

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

    2 Citations (Scopus)

    Abstract

    In this paper we propose a model of neural networks ensemble consisting of a number of MLPs, that deals with an imperfect learning supervisor that occasionally produces incorrect teacher signals. It is known that a conventional unitary neural network will not learn optimally from this kind of supervisor. We consider that the imperfect supervisor generates two kinds of input-output relations, the correct relation and the incorrect one. The learning characteristics of the proposed model allows the ensemble to automatically train one of its members to learn only from the correct input-output relation, producing a neural network that can to some extent tolerate the imperfection of the super- visor.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages188-197
    Number of pages10
    Volume2096
    ISBN (Print)3540422846, 9783540422846
    Publication statusPublished - 2001
    Event2nd International Workshop on Multiple Classifier Systems, MCS 2001 - Cambridge, United Kingdom
    Duration: 2001 Jul 22001 Jul 4

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2096
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other2nd International Workshop on Multiple Classifier Systems, MCS 2001
    CountryUnited Kingdom
    CityCambridge
    Period01/7/201/7/4

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    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Hartono, P., & Hashimoto, S. (2001). Learning-data selection mechanism through neural networks ensemble. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2096, pp. 188-197). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2096). Springer Verlag.