A unified model for support vector machine and support vector data description

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    5 Citations (Scopus)
    4 Downloads (Pure)

    Abstract

    Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD.
    Original languageEnglish
    Title of host publicationThe 2012 International Joint Conference on Neural Networks (IJCNN)
    EditorsHussein Abbass
    Place of PublicationNew York
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1984-1991
    Number of pages8
    ISBN (Print)9781467314893
    DOIs
    Publication statusPublished - 2012
    EventWCCI 2012 IEEE World Congress on Computational Intelligence - Brisbane, Brisbane, Australia
    Duration: 10 Jun 201215 Jun 2012

    Conference

    ConferenceWCCI 2012 IEEE World Congress on Computational Intelligence
    CountryAustralia
    CityBrisbane
    Period10/06/1215/06/12

    Fingerprint

    Data description
    Support vector machines
    Pattern recognition
    Experiments

    Cite this

    Le, T., Tran, D., Ma, W., & Sharma, D. (2012). A unified model for support vector machine and support vector data description. In H. Abbass (Ed.), The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1984-1991). New York: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2012.6252642
    Le, Trung ; Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / A unified model for support vector machine and support vector data description. The 2012 International Joint Conference on Neural Networks (IJCNN). editor / Hussein Abbass. New York : IEEE, Institute of Electrical and Electronics Engineers, 2012. pp. 1984-1991
    @inproceedings{638df81761734a93b9add96a6ca5352b,
    title = "A unified model for support vector machine and support vector data description",
    abstract = "Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD.",
    keywords = "Support Vector Machine, Support Vector Data Description",
    author = "Trung Le and Dat Tran and Wanli Ma and Dharmendra Sharma",
    year = "2012",
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    language = "English",
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    Le, T, Tran, D, Ma, W & Sharma, D 2012, A unified model for support vector machine and support vector data description. in H Abbass (ed.), The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, Institute of Electrical and Electronics Engineers, New York, pp. 1984-1991, WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane, Australia, 10/06/12. https://doi.org/10.1109/IJCNN.2012.6252642

    A unified model for support vector machine and support vector data description. / Le, Trung; Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

    The 2012 International Joint Conference on Neural Networks (IJCNN). ed. / Hussein Abbass. New York : IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 1984-1991.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AU - Ma, Wanli

    AU - Sharma, Dharmendra

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    N2 - Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD.

    AB - Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD.

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    KW - Support Vector Data Description

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    DO - 10.1109/IJCNN.2012.6252642

    M3 - Conference contribution

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    Le T, Tran D, Ma W, Sharma D. A unified model for support vector machine and support vector data description. In Abbass H, editor, The 2012 International Joint Conference on Neural Networks (IJCNN). New York: IEEE, Institute of Electrical and Electronics Engineers. 2012. p. 1984-1991 https://doi.org/10.1109/IJCNN.2012.6252642