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Network Boosting for BCI Applications

  • Shijun Wang
  • Zhonglin Lin
  • Changshui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shijun Wang
    • 1
  • Zhonglin Lin
    • 1
  • Changshui Zhang
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

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