The Brain & Neural Networks
Online ISSN : 1883-0455
Print ISSN : 1340-766X
ISSN-L : 1340-766X
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Volume 16, Issue 3
Displaying 1-14 of 14 articles from this issue
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  • A Fast Learning Vector Quantization for Kernel Machine
    Wataru Kasai, Osamu Hasegawa
    2009 Volume 16 Issue 3 Pages 149-157
    Published: September 05, 2009
    Released on J-STAGE: October 30, 2009
    DOIhttps://doi.org/10.3902/jnns.16.149
    JOURNAL FREE ACCESS
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    In this paper, we propose a fast learning algorithm of a support vector machine (SVM). Our work is based on the Learning Vector Quantization (LVQ) and we compress the data to perform properly in the context of clustered data margin maximization. For solving the problem faster, we propose the improved TOD algorithm, which is one of the simplest form of LVQ. Experimental results demonstrate that our method is as accurate as the existing implementation, but it is faster in most situations. We also show the extension of the proposed learning framework for online re-training problem.
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