Selecting training points for one-class support vector machines

Yuhua Li

    Research output: Contribution to journalArticlepeer-review

    63 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)1517-1522
    JournalPattern Recognition Letters
    Issue number11
    Publication statusPublished (in print/issue) - 1 Aug 2011


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