Abstract
Network Boosting is an ensemble learning method which combines learners together based on a network and can learn the target hypothesis asymptotically. We apply the approach to analyze data from the P300 speller paradigm. The result on the Data set II of BCI (Brain-computer interface) competition III shows that Network Boosting achieves higher classification accuracy than logistic regression, SVM, Bagging and AdaBoost.
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References
Wolpaw, J., et al.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prothesis utilizing event-related brain potentials. Electroenceph. Clin. Neurophysiol. 70, 510–523 (1988)
Donchin, E., et al.: The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehabi. Eng. 8, 174–179 (2000)
Wang, S.J., Zhang, C.S.: Network game and boosting. In: The 16th European Conference on Machine Learning (2005)
Wang, S.J., Zhang, C.S.: Weighted competition scale-free network. Phys. Rev. EÂ 70, 066127 (2004)
Albert, R., Barabási, A.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002)
http://ida.first.fraunhofer.de/projects/bci/competition_iii/
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, S., Lin, Z., Zhang, C. (2005). Network Boosting for BCI Applications. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds) Discovery Science. DS 2005. Lecture Notes in Computer Science(), vol 3735. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563983_38
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DOI: https://doi.org/10.1007/11563983_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29230-2
Online ISBN: 978-3-540-31698-5
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