An on-line learning mechanism is proposed for unsupervised data. Using a similarity threshold and local error based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of online non-stationary data distribution. The definition of a utility parameter -"error-radius" - enables this system to learn the number of nodes needed to solve a task. The usage of a new technique for removing nodes in low probability density regions can separate the clusters with low-density overlaps and dynamically eliminate noise in the input data. Experiment results show that this system can report a reasonable number of clusters and represent the topological structure of unsupervised on-line data with no prior conditions such as a suitable number of nodes or a good initial codebook.


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    Titel :

    An on-line learning mechanism for unsupervised classification and topology representation


    Beteiligte:
    Furao, S. (Autor:in) / Hasegawa, O. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    1030416 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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