Maximal Margin Learning Vector Quantisation

Dat TRAN, Van Nguyen, Wanli MA

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

    Abstract

    Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method
    Original languageEnglish
    Title of host publicationThe 2013 International Joint Conference on Neural Networks (IJCNN)
    EditorsPlamen Angelov, Daniel Levine
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1668-1673
    Number of pages6
    Volume1
    ISBN (Print)9781467361293
    DOIs
    Publication statusPublished - 2013
    Event2013 International Joint Conference on Neural Networks (IJCNN) - Dallas, Texas, United States
    Duration: 4 Aug 20139 Aug 2013

    Conference

    Conference2013 International Joint Conference on Neural Networks (IJCNN)
    CountryUnited States
    CityTexas
    Period4/08/139/08/13

    Fingerprint

    Vector quantization
    Pattern recognition
    Experiments

    Cite this

    TRAN, D., Nguyen, V., & MA, W. (2013). Maximal Margin Learning Vector Quantisation. In P. Angelov, & D. Levine (Eds.), The 2013 International Joint Conference on Neural Networks (IJCNN) (Vol. 1, pp. 1668-1673). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2013.6706940
    TRAN, Dat ; Nguyen, Van ; MA, Wanli. / Maximal Margin Learning Vector Quantisation. The 2013 International Joint Conference on Neural Networks (IJCNN). editor / Plamen Angelov ; Daniel Levine. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1668-1673
    @inproceedings{ca3b25e010ec48c7a28fd8baf44b26f3,
    title = "Maximal Margin Learning Vector Quantisation",
    abstract = "Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method",
    keywords = "Learning Vector Quantisation",
    author = "Dat TRAN and Van Nguyen and Wanli MA",
    year = "2013",
    doi = "10.1109/IJCNN.2013.6706940",
    language = "English",
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    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
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    TRAN, D, Nguyen, V & MA, W 2013, Maximal Margin Learning Vector Quantisation. in P Angelov & D Levine (eds), The 2013 International Joint Conference on Neural Networks (IJCNN). vol. 1, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 1668-1673, 2013 International Joint Conference on Neural Networks (IJCNN), Texas, United States, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6706940

    Maximal Margin Learning Vector Quantisation. / TRAN, Dat; Nguyen, Van; MA, Wanli.

    The 2013 International Joint Conference on Neural Networks (IJCNN). ed. / Plamen Angelov; Daniel Levine. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1668-1673.

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

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    T1 - Maximal Margin Learning Vector Quantisation

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    AU - Nguyen, Van

    AU - MA, Wanli

    PY - 2013

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    N2 - Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method

    AB - Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method

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    U2 - 10.1109/IJCNN.2013.6706940

    DO - 10.1109/IJCNN.2013.6706940

    M3 - Conference contribution

    SN - 9781467361293

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    SP - 1668

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    TRAN D, Nguyen V, MA W. Maximal Margin Learning Vector Quantisation. In Angelov P, Levine D, editors, The 2013 International Joint Conference on Neural Networks (IJCNN). Vol. 1. USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1668-1673 https://doi.org/10.1109/IJCNN.2013.6706940