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Large margin vector quantization

Buckingham, Lawrence I. & Geva, Shlomo (2000) Large margin vector quantization. In Proceedings of the Pacific Knowledge Acquisition Workshop (PKAW 2000), Coogee Beach, Sydney.

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    In this paper we describe the Large Margin Vector Quantization algorithm (LMVQ), which uses gradient ascent to maximise the margin of a radial basis function classifier. We present a derivation of the algorithm, which proceeds from an estimate of the class-conditional probability densities. We show that the key behaviour of Kohonen's well-known LVQ2 and LVQ3 algorithms emerge as natural consequences of our formulation. We compare the performance of LMVQ with that of Kohonen's LVQ algorithms on an artificial classification problem and several well known benchmark classification tasks. We find that the classifiers produced by LMVQ attain a level of accuracy that compares well with those obtained via LVQ1, LVQ2 and LVQ3, with reduced storage complexity. We indicate future directions of enquiry based on the large margin approach to Learning Vector Quantization.

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    ID Code: 34210
    Item Type: Conference Paper
    Keywords: vector quantization, classification, LVQ, Maximum Margin
    Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Optimisation (010303)
    Divisions: Past > Schools > Computer Science
    Past > QUT Faculties & Divisions > Faculty of Science and Technology
    Copyright Owner: Copyright 2000 Lawrence I. Buckingham and Shlomo Geva
    Deposited On: 24 Aug 2010 10:52
    Last Modified: 24 Aug 2010 10:52

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