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. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Wang, S., Lin, Z., & Zhang, C. (2005). Network boosting for BCI applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3735 LNAI, pp. 386–388). Springer Verlag. https://doi.org/10.1007/11563983_38
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