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

Trung Le; Dat Tran; Tuan Hoang; Dharmendra Sharma

doi:10.1007/978-3-642-34487-9_24

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

Trung Le, Dat Tran, Tuan Hoang, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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 language	English
Title of host publication	Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages	191-198
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-34487-9_24
Publication status	Published - 2012
Externally published	Yes
Event	International Conference on Neural Information Processing 2012 - Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012 Conference number: 19th https://link.springer.com/book/10.1007/978-3-642-34500-5 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Number	PART 3
Volume	7665
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2012
Abbreviated title	ICONIP 2012
Country	Qatar
City	Doha
Period	12/11/12 → 15/11/12
Internet address	https://link.springer.com/book/10.1007/978-3-642-34500-5 (Proceedings)

Keywords

Generalised Learning Vector Quantisation
Kernel Method
Learning Vector Quantisation
Maximising Margin

Access to Document

10.1007/978-3-642-34487-9_24

Link to publication in Scopus

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

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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 Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 3 edn, Lecture Notes in Computer Science , no. PART 3, vol. 7665 , pp. 191-198, International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34487-9_24

Maximal margin approach to kernel generalised learning vector quantisation for brain-computer interface. / Le, Trung; Tran, Dat; Hoang, Tuan; Sharma, Dharmendra.

Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 3. ed. 2012. p. 191-198 (Lecture Notes in Computer Science ; Vol. 7665 , No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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