<p>Values for the tuning parameters for each algorithm were selected using 10-fold cross validation on the voxels in the training set and validation of the algorithms was performed on a separate set.</p
<p>Summary of the labeling accuracy obtained for the training set, for all the discrimination proble...
<p>10-Fold Cross Validation Accuracies of the classifiers applied to the Artificial dataset.</p
<p>(1) Feature Extraction: the information from the beta images were transformed into an input vecto...
<p>Subjects were randomly assigned to the training or validation set. All training, including tuning...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
<p>The number of predicted phenotypes and overall prediction rates are given for each of the three “...
<p>Machine learning algorithms used and a short description of their training parameters.</p
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>Summary of Classifiers, Features, Validation Techniques and Sample Sizes used in this study.</p
<p>For each phenotype (column 1), the optimal mode of classification (“BAGS” or “C”) (second column)...
Labels are generated using inputs from a variety of models, which are then combined into a single so...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
*<p>The latter number represents standard deviation from 10 training set;</p>**<p>the lower right co...
<p>Summary of the labeling accuracy obtained for the training set, for all the discrimination proble...
<p>10-Fold Cross Validation Accuracies of the classifiers applied to the Artificial dataset.</p
<p>(1) Feature Extraction: the information from the beta images were transformed into an input vecto...
<p>Subjects were randomly assigned to the training or validation set. All training, including tuning...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
<p>The number of predicted phenotypes and overall prediction rates are given for each of the three “...
<p>Machine learning algorithms used and a short description of their training parameters.</p
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>Summary of Classifiers, Features, Validation Techniques and Sample Sizes used in this study.</p
<p>For each phenotype (column 1), the optimal mode of classification (“BAGS” or “C”) (second column)...
Labels are generated using inputs from a variety of models, which are then combined into a single so...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
*<p>The latter number represents standard deviation from 10 training set;</p>**<p>the lower right co...
<p>Summary of the labeling accuracy obtained for the training set, for all the discrimination proble...
<p>10-Fold Cross Validation Accuracies of the classifiers applied to the Artificial dataset.</p
<p>(1) Feature Extraction: the information from the beta images were transformed into an input vecto...