A multiclass classification method by distance mapping learning network

K. Suzuki, S. Hashimoto

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

    2 Citations (Scopus)

    Abstract

    We propose a method of multiclass classification by utilizing a distance mapping learning network that is a distance-based multilayer perceptron The network can obtain the non-linear mapping between the input objects and the outputs by providing a pair of objects and the desired distance between them. It thus realizes multiclass classification based on pairwise classifications iteratively. We show the validity of the model with two classification problems: Iris classification and facial expression classification.

    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.
    Pages393-397
    Number of pages5
    Volume1
    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

    Suzuki, K., & Hashimoto, S. (2002). A multiclass classification method by distance mapping learning network. In ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (Vol. 1, pp. 393-397). [1202200] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1202200