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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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.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages191-198
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
NumberPART 3
Volume7665
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Neural Information Processing, ICONIP 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

Keywords

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

Cite this

Le, T., Tran, D., Hoang, T., & Sharma, D. (2012). Maximal margin approach to kernel generalised learning vector quantisation for brain-computer interface. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings (PART 3 ed., pp. 191-198). (Lecture Notes in Computer Science ; Vol. 7665 , No. PART 3). https://doi.org/10.1007/978-3-642-34487-9_24