MISC

査読有り
2010年

Sequential error rate evaluation of SSVEP classification problem with Bayesian sequential learning

Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB
  • Hideyuki Hara
  • ,
  • Atsushi Takemoto
  • ,
  • Yumi Dobashi
  • ,
  • Katsuki Nakamura
  • ,
  • Takashi Matsumoto

記述言語
英語
掲載種別
DOI
10.1109/ITAB.2010.5687773

An attempt was made to evaluate the Sequential Error Rate (SER) of an SSVEP classification problem with a Bayesian sequential learning algorithm. Sequential Error Rate refers to the average classification error rate windowed over a short trial period. The algorithm was implemented by the Sequential Monte Carlo method. As opposed to the batch learning algorithm, the sequential learning algorithm does not divide the data into training and test datasets
rather, it starts learning with the first single trial data and proceeds with the learning sequentially using the rest of the data. The algorithm was tested against an SSVEP classification problem. The algorithm appeared functional © 2010 IEEE.

リンク情報
DOI
https://doi.org/10.1109/ITAB.2010.5687773
ID情報
  • DOI : 10.1109/ITAB.2010.5687773
  • SCOPUS ID : 79951631391

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