Outliers and data descriptions.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientific

    Abstract

    In previous research the support vector data description (SVDD) is proposed to solve the problem of one-class classification. In one-class classification, one set of data, called the target set, has to be distinguished from the rest of the feature space. In the original optimization of the support vector data description, two parameters have to be given beforehand by the user. In this paper a new, heuristic, error is defined. Minimizing this error, both free parameters in the SVDD can be determined without the use of example outlier objects. This paper shows under what circumstances the heuristic error correlates well with the true error.
    Original languageUndefined/Unknown
    Title of host publicationProc. ASCI 2001, 7th Annual Conf. of the Advanced School for Computing and Imaging.
    EditorsRL Lagendijk, JWJ Heijnsdijk, AD Pimentel, MHF Wilkinson
    Place of PublicationDelft
    PublisherASCI
    Pages234-241
    Number of pages8
    ISBN (Print)90-803086-6-8
    Publication statusPublished - 2001
    Event7th Annual Conf. of the Advanced School for Computing and Imaging, Heijen, NL. - Delft
    Duration: 30 May 20011 Jun 2001

    Publication series

    Name
    PublisherASCI

    Conference

    Conference7th Annual Conf. of the Advanced School for Computing and Imaging, Heijen, NL.
    Period30/05/011/06/01

    Keywords

    • ZX Int.klas.verslagjaar < 2002

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