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

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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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 language	English
Title of host publication	International Conference on Neural Information Processing (ICONIP 2012)
Subtitle of host publication	Lecture Notes in Computer Science
Editors	Tingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of Publication	Germany
Publisher	Springer
Pages	191-198
Number of pages	8
Volume	7665
ISBN (Electronic)	9783642344879
ISBN (Print)	9783642344800
DOIs	https://doi.org/10.1007/978-3-642-34487-9_24
Publication status	Published - 2012
Event	19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012

Conference

Conference	19th International Conference on Neural Information Processing 2012
Country/Territory	Qatar
City	Doha
Period	12/11/12 → 15/11/12

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

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 T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science (Vol. 7665, pp. 191-198). Springer. https://doi.org/10.1007/978-3-642-34487-9_24

Le, Trung ; Tran, Dat ; Hoang, Tuan et al. / Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface. International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. editor / Tingwen Huang ; Zhigang Zeng ; Chuandong Li ; Chi Sing Leung. Vol. 7665 Germany : Springer, 2012. pp. 191-198

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title = "Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface",

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.",

keywords = "Generalised Learning Vector Quantisation, Brain-Computer Interface, Learning Vector Quantisation, Kernel Method, Maximising Margin",

author = "Trung Le and Dat Tran and Tuan Hoang and Dharmendra Sharma",

year = "2012",

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booktitle = "International Conference on Neural Information Processing (ICONIP 2012)",

publisher = "Springer",

address = "Netherlands",

note = "19th International Conference on Neural Information Processing 2012 ; Conference date: 12-11-2012 Through 15-11-2012",

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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 T Huang, Z Zeng, C Li & CS Leung (eds), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. vol. 7665, Springer, Germany, pp. 191-198, 19th 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 et al.
International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Vol. 7665 Germany: Springer, 2012. p. 191-198.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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T1 - Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface

AU - Le, Trung

AU - Tran, Dat

AU - Hoang, Tuan

AU - Sharma, Dharmendra

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N2 - 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.

AB - 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.

KW - Generalised Learning Vector Quantisation

KW - Brain-Computer Interface

KW - Learning Vector Quantisation

KW - Kernel Method

KW - Maximising Margin

UR - https://link.springer.com/chapter/10.1007%2F978-3-642-34487-9_24

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M3 - Conference contribution

SN - 9783642344800

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EP - 198

BT - International Conference on Neural Information Processing (ICONIP 2012)

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A2 - Zeng, Zhigang

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Le T, Tran D, Hoang T, Sharma D. Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface. In Huang T, Zeng Z, Li C, Leung CS, editors, International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. Vol. 7665. Germany: Springer. 2012. p. 191-198 doi: 10.1007/978-3-642-34487-9_24

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

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