Effective learning in noisy environment using neural network ensemble

Pitoyo Hartono, Shuji Hashimoto

    研究成果: Conference contribution

    1 引用 (Scopus)

    抜粋

    We have previously proposed a model of neural network ensemble composed of a number of Multi Layer Perceptrons (MLP). The ensemble is trained so that each member has a unique expertise. It is also provided with a mechanism to automatically select the most relevant member with respect to the given environment, enabling the ensemble to adapt effectively in changing environment. In this research we trained the ensemble with noisy training data set, which is a training set that contains a particular percentage of contradictionary (false) data. Based on the members' expertise the ensemble has the ability to distinguish contradictionary data and treat such kind of data set as one unique environment that differs from the clean environment formed by correct data. In the training process the ensemble will automatically select one of its member to be trained in the clean environment and switch to another member whenever a contradictionary data is given, resulting that one of the ensemble member will be successfully adapting the clean environment.

    元の言語English
    ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
    出版場所Piscataway, NJ, United States
    出版者IEEE
    ページ179-184
    ページ数6
    2
    出版物ステータスPublished - 2000
    イベントInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
    継続期間: 2000 7 242000 7 27

    Other

    OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
    Como, Italy
    期間00/7/2400/7/27

    ASJC Scopus subject areas

    • Software

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  • これを引用

    Hartono, P., & Hashimoto, S. (2000). Effective learning in noisy environment using neural network ensemble. : Proceedings of the International Joint Conference on Neural Networks (巻 2, pp. 179-184). IEEE.