A self-organizing neural network model that resembles Kohonen's feature map model is presented in this paper. Unlike conventional feature maps which require static neighborhood relations to be defined a priori, our model is characterized by its use of dynamic neighborhood relations which change as learning proceeds. In particular, the neighborhood relations between neurons in a feature map are determined by an underlying Gabriel graph, which represents two neurons as neighbors if and only if the smallest hypersphere enclosing the two corresponding weight vectors encloses no other weight vectors. We show empirically that this network model works consistently well in the vector quantization tasks tested. More importantly, our model can adapt to data manifolds which may not be handled well using conventional self-organizing feature maps.<>


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Gabriel networks: self-organizing neural networks for adaptive vector quantization


    Beteiligte:
    Kwok-Leung Mou (Autor:in) / Dit-Yan Yeung (Autor:in)


    Erscheinungsdatum :

    01.01.1994


    Format / Umfang :

    275190 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Gabriel Networks: Self-Organizing Neural Networks for Adaptive Vector Quantization

    Mou, K. L. / Yeung, D. Y. / IEEE; Hong Kong Chapter of Signal Processing | British Library Conference Proceedings | 1994


    View-based route-learning with self-organizing neural networks

    Hamze, F. / Clark, J. J. | British Library Online Contents | 2001


    Towards Growing Self-Organizing Neural Networks with Fixed Dimensionality

    Guojian Cheng / Tianshi Liu / Jiaxin Han et al. | BASE | 2008

    Freier Zugriff

    Adaptive Vector-Quantization Scheme

    Cheung, Kar-Ming | NTRS | 1992