Generalized Support Vector Machine for Brain-Computer Interface

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

    2 Citations (Scopus)
    6 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 of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.
    Original languageEnglish
    Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2011)
    Subtitle of host publicationLecture Notes in Computer Science
    EditorsBao-Liang Lu, Liqing Zhang, James Kwok
    Place of PublicationGermany
    PublisherSpringer
    Pages692-700
    Number of pages9
    Volume7062
    ISBN (Electronic)9783642249556
    ISBN (Print)9783642249549
    DOIs
    Publication statusPublished - 2011
    Event18th International Conference on Neural Information Processing - Shanghai, Shanghai, China
    Duration: 13 Nov 201117 Nov 2011

    Conference

    Conference18th International Conference on Neural Information Processing
    CountryChina
    CityShanghai
    Period13/11/1117/11/11

    Fingerprint

    Data description
    Brain computer interface
    Support vector machines
    Pattern recognition
    Experiments

    Cite this

    Tran, D., Ma, W., & Sharma, D. (2011). Generalized Support Vector Machine for Brain-Computer Interface. In B-L. Lu, L. Zhang, & J. Kwok (Eds.), International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science (Vol. 7062, pp. 692-700). Germany: Springer. https://doi.org/10.1007/978-3-642-24955-6_82
    Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / Generalized Support Vector Machine for Brain-Computer Interface. International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. editor / Bao-Liang Lu ; Liqing Zhang ; James Kwok. Vol. 7062 Germany : Springer, 2011. pp. 692-700
    @inproceedings{9f04034535e14141b1b6b05e83140ec6,
    title = "Generalized Support Vector Machine for Brain-Computer Interface",
    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 of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.",
    keywords = "Machine Learning, Brain-Computer Interface",
    author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
    year = "2011",
    doi = "10.1007/978-3-642-24955-6_82",
    language = "English",
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    publisher = "Springer",
    address = "Netherlands",

    }

    Tran, D, Ma, W & Sharma, D 2011, Generalized Support Vector Machine for Brain-Computer Interface. in B-L Lu, L Zhang & J Kwok (eds), International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. vol. 7062, Springer, Germany, pp. 692-700, 18th International Conference on Neural Information Processing, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24955-6_82

    Generalized Support Vector Machine for Brain-Computer Interface. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

    International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. ed. / Bao-Liang Lu; Liqing Zhang; James Kwok. Vol. 7062 Germany : Springer, 2011. p. 692-700.

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

    TY - GEN

    T1 - Generalized Support Vector Machine for Brain-Computer Interface

    AU - Tran, Dat

    AU - Ma, Wanli

    AU - Sharma, Dharmendra

    PY - 2011

    Y1 - 2011

    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 of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

    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 of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

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    SP - 692

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    BT - International Conference on Neural Information Processing (ICONIP 2011)

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    A2 - Zhang, Liqing

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    Tran D, Ma W, Sharma D. Generalized Support Vector Machine for Brain-Computer Interface. In Lu B-L, Zhang L, Kwok J, editors, International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. Vol. 7062. Germany: Springer. 2011. p. 692-700 https://doi.org/10.1007/978-3-642-24955-6_82