QUT ePrints

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.

[img] Workshop Paper (PDF 258kB)
Accepted Version.

    View at publisher

    Abstract

    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.

    Impact and interest:

    Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

    These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

    Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

    Full-text downloads:

    62 since deposited on 24 Aug 2010
    15 in the past twelve months

    Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

    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

    Export: EndNote | Dublin Core | BibTeX

    Repository Staff Only: item control page