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    Support Vector Machine: Principles, Parameters, and Applications

    Access Status
    Fulltext not available
    Authors
    Gholami, Raoof
    Fakhari, N.
    Date
    2017
    Type
    Book Chapter
    
    Metadata
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    Citation
    Gholami, R. and Fakhari, N. 2017. Support Vector Machine: Principles, Parameters, and Applications. In Handbook of Neural Computation, 515-535.
    Source Title
    Handbook of Neural Computation
    DOI
    10.1016/B978-0-12-811318-9.00027-2
    ISBN
    9780128113196
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/58416
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 Elsevier Inc. All rights reserved. Support Vector Machine (SVM) has been introduced in the late 1990s and successfully applied to many engineering related applications. In this chapter, attempts were made to introduce the SVM, its principles, structures, and parameters. The issue of selecting a kernel function and other associated parameters of SVMs was also raised and applications from different petroleum and mining related tasks were brought to show how those parameters can be properly selected. It seems that the cross-validation approach would be the best technique for parameter selections of SVMs but few other concerns such as running time must not be neglected.

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