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.
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References
Babiloni, F., Cichocki, A., Gao, S.: Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications. Computational Intelligence and Neuroscience, 1–2 (2007)
Lotte, F.: PhD thesis: Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications (2008)
Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., Ritter, H.: BCI competition 2003 data set IIb: support vector machines for the P300 speller paradigm. IEEE Trans. Biomed. Eng. 51, 1073–1076 (2004)
Rakotomamonjy, A., Guigue, V.: BCI competition III: dataset II- ensemble of SVMs for BCI P300 speller. IEEE Trans. Biomed. Eng. 55, 1147–1154 (2008)
Kalcher, J., Flotzinger, D., Neuper, C., Glly, S., Pfurtscheller, G.: Graz brain- computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns. Med. Bio. Eng. Computing 34(5), 382–388 (1996)
Solhjoo, S., Moradi, M.H.: Mental task recognition: A comparison between some of classification methods. In: BIOSIGNAL Int. Conf. EURASIP, pp. 24–26 (2004)
Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural System and Rehabilitation Eng. 11, 141–144 (2003)
Bostanov, V.: BCI competition 2003data sets Ib and IIb: Feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans. Biomed. Eng. 51, 1057–1061 (2004)
Kuncheva, L.I., Rodriguez, J.J.: Classifier ensembles for fMRI data analysis: an experiment. Magnetic Resonance Imaging 28(4), 583–593 (2010)
Hoang, T., Tran, D., Nguyen, P., Huang, X., Sharma, D.: Experiments on using Combined Short Window Bivariate Autoregression for EEG Classification. In: Proc. IEEE Internatinal Conferrence on Neural Engineering, pp. 372–375 (2011)
Anderson, C.W., Stolz, E.A., Shamsunder, S.: Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Trans. Biomed. Eng. 45, 277–286 (1998)
Brunner, C., Billinger, M., Neuper, C.: A Comparison of Univariate, Multivariate, Bilinear Autoregressive, and Bandpower Features for Brain-Computer Interfaces. In: Fourth International BCI Meeting, Poster B-22 (2010)
Vapnik, V.: The nature of statistical learning theory. Springer, Heidelberg (1995)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54, 45–56 (2004)
Le, T., Tran, D., Ma, W., Sharma, D.: An Optimal Sphere and Two Large Margins Approach for Novelty Detection. In: Proc. IEEE World Congress on Computational Intelligence (WCCI), pp. 909–914 (2010)
Wu, M., Ye, J.: A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers. IEEE Trans. Pattern Analysis & Machine Intelligence 31, 2088–2092 (2009)
BCI Competition II, http://www.bbci.de/competition/ii/
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Le, T., Tran, D., Hoang, T., Ma, W., Sharma, D. (2011). Generalised Support Vector Machine for Brain-Computer Interface. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_82
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DOI: https://doi.org/10.1007/978-3-642-24955-6_82
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