Adaptive neural network ensemble that learns from imperfect supervisor

P. Hartono, S. Hashimoto

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

    3 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2561-2565
    Number of pages5
    Volume5
    ISBN (Electronic)9810475241, 9789810475246
    DOIs
    Publication statusPublished - 2002
    Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
    Duration: 2002 Nov 182002 Nov 22

    Other

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

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    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

    Cite this

    Hartono, P., & Hashimoto, S. (2002). Adaptive neural network ensemble that learns from imperfect supervisor. In ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (Vol. 5, pp. 2561-2565). [1201957] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1201957