Generalized Support Vector Machine for Brain-Computer Interface

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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
Number of pages9
ISBN (Electronic)9783642249556
ISBN (Print)9783642249549
Publication statusPublished - 2011
Event18th International Conference on Neural Information Processing - Shanghai, Shanghai, China
Duration: 13 Nov 201117 Nov 2011


Conference18th International Conference on Neural Information Processing


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