Generalized Support Vector Machine for Brain-Computer Interface

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)
6 Downloads (Pure)

Abstract

Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2011)
Subtitle of host publicationLecture Notes in Computer Science
EditorsBao-Liang Lu, Liqing Zhang, James Kwok
Place of PublicationGermany
PublisherSpringer
Pages692-700
Number of pages9
Volume7062
ISBN (Electronic)9783642249556
ISBN (Print)9783642249549
DOIs
Publication statusPublished - 2011
Event18th International Conference on Neural Information Processing - Shanghai, Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Conference

Conference18th International Conference on Neural Information Processing
CountryChina
CityShanghai
Period13/11/1117/11/11

Fingerprint

Data description
Brain computer interface
Support vector machines
Pattern recognition
Experiments

Cite this

Tran, D., Ma, W., & Sharma, D. (2011). Generalized Support Vector Machine for Brain-Computer Interface. In B-L. Lu, L. Zhang, & J. Kwok (Eds.), International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science (Vol. 7062, pp. 692-700). Germany: Springer. https://doi.org/10.1007/978-3-642-24955-6_82
Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / Generalized Support Vector Machine for Brain-Computer Interface. International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. editor / Bao-Liang Lu ; Liqing Zhang ; James Kwok. Vol. 7062 Germany : Springer, 2011. pp. 692-700
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title = "Generalized Support Vector Machine for Brain-Computer Interface",
abstract = "Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.",
keywords = "Machine Learning, Brain-Computer Interface",
author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
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isbn = "9783642249549",
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Tran, D, Ma, W & Sharma, D 2011, Generalized Support Vector Machine for Brain-Computer Interface. in B-L Lu, L Zhang & J Kwok (eds), International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. vol. 7062, Springer, Germany, pp. 692-700, 18th International Conference on Neural Information Processing, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24955-6_82

Generalized Support Vector Machine for Brain-Computer Interface. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. ed. / Bao-Liang Lu; Liqing Zhang; James Kwok. Vol. 7062 Germany : Springer, 2011. p. 692-700.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Generalized Support Vector Machine for Brain-Computer Interface

AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

PY - 2011

Y1 - 2011

N2 - Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

AB - Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

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BT - International Conference on Neural Information Processing (ICONIP 2011)

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Tran D, Ma W, Sharma D. Generalized Support Vector Machine for Brain-Computer Interface. In Lu B-L, Zhang L, Kwok J, editors, International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. Vol. 7062. Germany: Springer. 2011. p. 692-700 https://doi.org/10.1007/978-3-642-24955-6_82