Selecting training points for one-class support vector machines

Yuhua Li

    Research output: Contribution to journalArticle

    45 Citations (Scopus)

    Abstract

    This paper proposes a training points selection method for one-class support vector machines. It exploits the feature of a trained one-class SVM, which uses points only residing on the exterior region of data distribution as support vectors. Thus, the proposed training set reduction method selects the so-called extreme points which sit on the boundary of data distribution, through local geometry and k-nearest neighbours. Experimental results demonstrate that the proposed method can reduce training set considerably, while the obtained model maintains generalization capability to the level of a model trained on the full training set, but uses less support vectors and exhibits faster training speed.
    LanguageEnglish
    Pages1517-1522
    JournalPattern Recognition Letters
    Volume32
    Issue number11
    DOIs
    Publication statusPublished - 1 Aug 2011

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    Support vector machines
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    Cite this

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    title = "Selecting training points for one-class support vector machines",
    abstract = "This paper proposes a training points selection method for one-class support vector machines. It exploits the feature of a trained one-class SVM, which uses points only residing on the exterior region of data distribution as support vectors. Thus, the proposed training set reduction method selects the so-called extreme points which sit on the boundary of data distribution, through local geometry and k-nearest neighbours. Experimental results demonstrate that the proposed method can reduce training set considerably, while the obtained model maintains generalization capability to the level of a model trained on the full training set, but uses less support vectors and exhibits faster training speed.",
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    Selecting training points for one-class support vector machines. / Li, Yuhua.

    In: Pattern Recognition Letters, Vol. 32, No. 11, 01.08.2011, p. 1517-1522.

    Research output: Contribution to journalArticle

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