<p>Plot shows the pairwise differences in performance among classifiers. The horizontal scale shows the average rank of each classifier, with smaller ranks indicating better performance. Classifiers connected by a dark line had statistically identical performance at the <i>p</i> = 0.05 level.</p
Description ROC graphs, sensitivity/specificity curves, lift charts,and precision/recall plots are p...
Comparison of using different data compositions of synthetic images for training the classifier and ...
<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hi...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>A comparison of the training performance, test accuracy, and uncertainty among classifiers in var...
<p>A) Mean and standard error of the report scores to the post-hoc questionnaire for each training d...
<p>LS is identified as the first day with significant performance, i.e. with a significant differenc...
<p>Overall performance comparison against each classifier for Drebin Dataset.</p
Comparison of correlations between training and position specificity indices for different parts of ...
<p>The Figure illustrates in the top row with black dots the classification accuracy with the separa...
<p>The classification performance is compared between the four-layer network features in Method I an...
<p>The horizontal axis represents the number of characters processed. The vertical axis represents h...
<p>Comparison of the prediction results of different basic classifiers by using varied numbers of su...
<p>Comparisons were made by means of a one sided, paired t-test, testing the hypothesis that the err...
Description ROC graphs, sensitivity/specificity curves, lift charts,and precision/recall plots are p...
Comparison of using different data compositions of synthetic images for training the classifier and ...
<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hi...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>A comparison of the training performance, test accuracy, and uncertainty among classifiers in var...
<p>A) Mean and standard error of the report scores to the post-hoc questionnaire for each training d...
<p>LS is identified as the first day with significant performance, i.e. with a significant differenc...
<p>Overall performance comparison against each classifier for Drebin Dataset.</p
Comparison of correlations between training and position specificity indices for different parts of ...
<p>The Figure illustrates in the top row with black dots the classification accuracy with the separa...
<p>The classification performance is compared between the four-layer network features in Method I an...
<p>The horizontal axis represents the number of characters processed. The vertical axis represents h...
<p>Comparison of the prediction results of different basic classifiers by using varied numbers of su...
<p>Comparisons were made by means of a one sided, paired t-test, testing the hypothesis that the err...
Description ROC graphs, sensitivity/specificity curves, lift charts,and precision/recall plots are p...
Comparison of using different data compositions of synthetic images for training the classifier and ...
<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hi...
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