Maximal margin approach to kernel generalised learning vector quantisation for brain-computer interface

Trung Le, Dat Tran, Tuan Hoang, Dharmendra Sharma

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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.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Number of pages8
EditionPART 3
Publication statusPublished - 2012
Externally publishedYes
EventInternational Conference on Neural Information Processing 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012
Conference number: 19th (Proceedings)

Publication series

NameLecture Notes in Computer Science
NumberPART 3
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Neural Information Processing 2012
Abbreviated titleICONIP 2012
Internet address


  • Generalised Learning Vector Quantisation
  • Kernel Method
  • Learning Vector Quantisation
  • Maximising Margin

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