The performance of the AdaBoost classifier with 6 selected features using the Leave-one-subject-out cross-validation method.</p
Comparison of classification accuracies of dataset 1 with different classifiers.</p
Performance comparison of different feature selection techniques on EN dataset in group AB.</p
The testing accuracies and execution time for six classifiers on different-scale datasets with noise...
<p>Single-trial subject dependent and subject independent AdaBoost classifier performance on identic...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>Performances of leave one feature out validations and using all six features on NS800 (5-fold cro...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>Performances of four different classification methods, combined with 4-fold and Leave-One-Out cro...
<p>Performance evaluation of various classifier and feature selection methods.</p
<p>Prediction performance of leave-one-out cross-validation based on different encoding methods.</p
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
Leave-One-Out cross-validation mean classification performance for AD versus CN of multi-measure fea...
<p>Comparison of classification results obtained through 5-fold cross validation with respect to dif...
<p>Binary SVM and AdaBoost classifier performance for average-ERP data using identical input feature...
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
Comparison of classification accuracies of dataset 1 with different classifiers.</p
Performance comparison of different feature selection techniques on EN dataset in group AB.</p
The testing accuracies and execution time for six classifiers on different-scale datasets with noise...
<p>Single-trial subject dependent and subject independent AdaBoost classifier performance on identic...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>Performances of leave one feature out validations and using all six features on NS800 (5-fold cro...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>Performances of four different classification methods, combined with 4-fold and Leave-One-Out cro...
<p>Performance evaluation of various classifier and feature selection methods.</p
<p>Prediction performance of leave-one-out cross-validation based on different encoding methods.</p
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
Leave-One-Out cross-validation mean classification performance for AD versus CN of multi-measure fea...
<p>Comparison of classification results obtained through 5-fold cross validation with respect to dif...
<p>Binary SVM and AdaBoost classifier performance for average-ERP data using identical input feature...
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
Comparison of classification accuracies of dataset 1 with different classifiers.</p
Performance comparison of different feature selection techniques on EN dataset in group AB.</p
The testing accuracies and execution time for six classifiers on different-scale datasets with noise...
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