<p>The correct rates (%) were derived with systematically varying number of labels (L), number of samples per label (N), feature conditions, and optimization algorithms. (A) Correct rates of each of label number conditions (differentiated by line colors) as a function of sample number conditions for ALL-feature and 2R–2L-optimization situation. (B) Correct rates at L = 7, N = 20 location (shown as a black arrow in the leftmost panel) as a function of feature conditions using 2R–2L optimization. (C) Correct rates L = 7, N = 20 location from different optimization functions. Error bars indicate standard error (<i>n</i> = 8 birds). *<i>p</i><0.05 (Tukey-Kramer HSD).</p
Performance of different models on PF4204 dataset using 10-fold cross-validation method.</p
<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....
10-fold cross-validation mean classification performance for MCI against CN of multi-functional feat...
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
<p>The table shows the cross-validation performance of our method on the labeled data points. The me...
The best performances are indicated in bold (the lower the better). These results are obtained using...
10-fold cross-validation mean classification performance for AD against CN of multi-functional featu...
<p>The boxplots represent the distribution of observed fraction of correct classification for all fe...
<p>Comparison of classification results obtained through 5-fold cross validation with respect to dif...
Performance of different models on PF2095 dataset using 10-fold cross-validation method.</p
<p>For the MLR, ANN and RF methods, 95% confidence intervals of the difference between the indicator...
Abstract Background Cross-validation (CV) is an effective method for estimating the prediction error...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>5 fold cross-validation classification performance, stability calculated as the Average Normalize...
Performance of different models on PF4204 dataset using 10-fold cross-validation method.</p
<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....
10-fold cross-validation mean classification performance for MCI against CN of multi-functional feat...
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
<p>The table shows the cross-validation performance of our method on the labeled data points. The me...
The best performances are indicated in bold (the lower the better). These results are obtained using...
10-fold cross-validation mean classification performance for AD against CN of multi-functional featu...
<p>The boxplots represent the distribution of observed fraction of correct classification for all fe...
<p>Comparison of classification results obtained through 5-fold cross validation with respect to dif...
Performance of different models on PF2095 dataset using 10-fold cross-validation method.</p
<p>For the MLR, ANN and RF methods, 95% confidence intervals of the difference between the indicator...
Abstract Background Cross-validation (CV) is an effective method for estimating the prediction error...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>5 fold cross-validation classification performance, stability calculated as the Average Normalize...
Performance of different models on PF4204 dataset using 10-fold cross-validation method.</p
<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....
10-fold cross-validation mean classification performance for MCI against CN of multi-functional feat...