<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The classification accuracies were averaged over ten participants.</p
Dynamic ensemble learning methods explore the use of different classifiers for different samples, th...
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>Results shown are for the case of 6-fold cross validation, which corresponds to a training sample...
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. The mean classification accuracies over ten p...
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. Then the data set A was classified by those c...
<p>The best accuracy among all for each algorithm and each repetition is written in bold and the wo...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>The reported values are <i>mean</i> ± <i>standard deviation</i>, where the variation is the resul...
<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP...
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
Our hypothesis is that building ensembles of small sets of strong classifiers constructed with diffe...
Dynamic ensemble learning methods explore the use of different classifiers for different samples, th...
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>Results shown are for the case of 6-fold cross validation, which corresponds to a training sample...
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. The mean classification accuracies over ten p...
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. Then the data set A was classified by those c...
<p>The best accuracy among all for each algorithm and each repetition is written in bold and the wo...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>The reported values are <i>mean</i> ± <i>standard deviation</i>, where the variation is the resul...
<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP...
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
Our hypothesis is that building ensembles of small sets of strong classifiers constructed with diffe...
Dynamic ensemble learning methods explore the use of different classifiers for different samples, th...
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>Results shown are for the case of 6-fold cross validation, which corresponds to a training sample...
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