<p>Performances of leave one feature out validations and using all six features on NS800 (5-fold cross validation, %).</p
10-fold cross-validation mean classification performance for AD against CN of multi-functional featu...
Performance of different models on PF2095 dataset using 10-fold cross-validation method.</p
<p>The cross-validation approaches for different kernels were run on our training set including 198 ...
<p>Performances of different feature combinations on NS800 (5-fold cross validation, %).</p
The performance of the AdaBoost classifier with 6 selected features using the Leave-one-subject-out ...
<p>Prediction performance of leave-one-out cross-validation based on different encoding methods.</p
Leave-One-Out cross-validation mean classification performance for AD versus CN of multi-measure fea...
Leave-one-out cross validation and Bootstrap .632+ estimator errors per features set.</p
<p>Performances of four different classification methods, combined with 4-fold and Leave-One-Out cro...
<p>Performances of using PSSM, SPSSM, NMR CS and DS_Profile features on NS800 (5-fold cross validati...
Leave-One-Out cross-validation mean classification performance for MCI against CN of multi-functiona...
<p>Performance of the different feature-sets on valence, development and evaluation-sets of 2015, 20...
<p>This overview shows the individual importance of each of the features in the overall classificati...
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
<p>Multicoil2 performance on trimer families in leave-family-out cross-validation.</p
10-fold cross-validation mean classification performance for AD against CN of multi-functional featu...
Performance of different models on PF2095 dataset using 10-fold cross-validation method.</p
<p>The cross-validation approaches for different kernels were run on our training set including 198 ...
<p>Performances of different feature combinations on NS800 (5-fold cross validation, %).</p
The performance of the AdaBoost classifier with 6 selected features using the Leave-one-subject-out ...
<p>Prediction performance of leave-one-out cross-validation based on different encoding methods.</p
Leave-One-Out cross-validation mean classification performance for AD versus CN of multi-measure fea...
Leave-one-out cross validation and Bootstrap .632+ estimator errors per features set.</p
<p>Performances of four different classification methods, combined with 4-fold and Leave-One-Out cro...
<p>Performances of using PSSM, SPSSM, NMR CS and DS_Profile features on NS800 (5-fold cross validati...
Leave-One-Out cross-validation mean classification performance for MCI against CN of multi-functiona...
<p>Performance of the different feature-sets on valence, development and evaluation-sets of 2015, 20...
<p>This overview shows the individual importance of each of the features in the overall classificati...
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
<p>Multicoil2 performance on trimer families in leave-family-out cross-validation.</p
10-fold cross-validation mean classification performance for AD against CN of multi-functional featu...
Performance of different models on PF2095 dataset using 10-fold cross-validation method.</p
<p>The cross-validation approaches for different kernels were run on our training set including 198 ...
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