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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    Title :

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


    Contributors:
    Furao, S. (author) / Hasegawa, O. (author)


    Publication date :

    2005-01-01


    Size :

    1030416 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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