Message length formulation of Support Vector Machines for binary classification - a preliminary scheme

Lara Kornienko, David L Dowe, David W Albrecht

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    2 Citations (Scopus)

    Abstract

    This paper presents a preliminary attempt at performing extrinsic binary classification by reformulating the Support Vector Machine (SVM) approach in a Bayesian Message Length framework. The reformulation uses the Minimum Message Length (MML) principle as a way of costing each hyperplane via a two-part message. This message defines a separating hyperplane. The length of this message is used as an objective function for a search through the hypothesis space of possible hyperplanes used to dichotomise a set of data points.

    Two preliminary MML implementations are presented here, which difier in the (Bayesian) coding schemes used and the search procedures. The generalisation ability of these two reformulations on both artificial and real data sets are compared against current implementations of Support Vector Machines - namely SVM light, the Lagrangian Support Vector Machine and SMOBR. It was found that, in general, all implementations improved as the size of the data sets increased. The MML implementations tended to perform best on the inseparable data sets and the real data set. Our preliminary MML scheme showed itself to be a strong competitor against the classical SVM, despite inefficiencies in the current scheme
    Original languageEnglish
    Title of host publicationAI 2002: Advances in Artificial Intelligence
    Subtitle of host publication15th Australian Joint Conference on Artificial Intelligence Canberra, Australia, December 2-6, 2002 Proceedings
    EditorsBob McKay, John Slaney
    Place of PublicationBerlin Germany
    PublisherSpringer
    Pages119-130
    Number of pages12
    ISBN (Print)3540001972
    DOIs
    Publication statusPublished - 2002
    EventAustralasian Joint Conference on Artificial Intelligence 2002 - Canberra, Australia
    Duration: 2 Dec 20026 Dec 2002
    Conference number: 15th
    https://link.springer.com/book/10.1007/3-540-36187-1 (Proceedings)

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume2557
    ISSN (Print)0302-9743

    Conference

    ConferenceAustralasian Joint Conference on Artificial Intelligence 2002
    Abbreviated titleAI 2002
    CountryAustralia
    CityCanberra
    Period2/12/026/12/02
    Internet address

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