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Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface

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Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7665))

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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 yield 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 to Kernel Generalised Learning Vector Quantisation algorithm which inherits the merits of KGLVQ and follows the maximal margin principle to favour the generalisation capability. Experiments performed on the well-known data set III of BCI competition II show promising classification results for the proposed method.

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Le, T., Tran, D., Hoang, T., Sharma, D. (2012). Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_24

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  • DOI: https://doi.org/10.1007/978-3-642-34487-9_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34486-2

  • Online ISBN: 978-3-642-34487-9

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