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Evaluation parameters of ensemble classifiers with overlapped partitioning on data set B (BCI competition III data set II).

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posted on 02.04.2014, 03:35 by Akinari Onishi, Kiyohisa Natsume

The ensemble classifiers were trained on limited training data (900 training data ) or full training data (15300 training data). The number of weak learners and the number of blocks were parameters used in the overlapped partitioning. These evaluation methods and the parameters determine the amount of training data for a weak learner in an ensemble classifier. The number of training data for a weak learner (#training data for a weak learner) can be computed by given training ERPs × /.

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