Adaptive neural network ensemble that learns from imperfect supervisor

P. Hartono, S. Hashimoto

    研究成果: Conference contribution

    3 被引用数 (Scopus)

    抄録

    In training supervised-type neural networks, the quality of the training data is one of the most important factors in deciding the quality of the neural networks. Unfortunately, in real world problems, error-free training data are not always easy to obtain. For complex data, it is always possible that erroneous training samples are included, causing to decrease the performance of the neural networks. In this research, we propose a model of neural network ensemble that, through a competition mechanism, has an ability to automatically train one of its members to learn only from the correct training patterns, thus minimizing the effect of the imperfect data.

    本文言語English
    ホスト出版物のタイトルICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ2561-2565
    ページ数5
    5
    ISBN(電子版)9810475241, 9789810475246
    DOI
    出版ステータスPublished - 2002
    イベント9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
    継続期間: 2002 11 182002 11 22

    Other

    Other9th International Conference on Neural Information Processing, ICONIP 2002
    国/地域Singapore
    CitySingapore
    Period02/11/1802/11/22

    ASJC Scopus subject areas

    • コンピュータ ネットワークおよび通信
    • 情報システム
    • 信号処理

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