This paper presents a learning scheme called stochastically competitive learning algorithm (SCLA) for globally optimal vector quantizer design. The SCLA incorporates the idea of stochastic relaxation into the on-line learning scheme of the Kohonen Learning Algorithm (KLA). The key of the SCLA is to replace the Euclidean winner rule with the stochastic competition such that at a given instant any codevector may be updated according to a probability related with its distance to the input. With computer simulations, the effectiveness of the SCLA has been demonstrated by comparing its performance with that of the GLA.<>


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    Title :

    Globally optimal vector quantizer design using stochastically competitive learning algorithm


    Contributors:
    Hao Bi (author) / Guangguo Bi (author) / Yimin Mao (author)


    Publication date :

    1994-01-01


    Size :

    279879 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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