<p>The red line represents the average performance of the initial classifier trained with the original categories of the JCVI-CMR ontology. The blue line, represents the average performance of the final classifier trained with a rearranged version of the ontology where noisy subcategories were merged together to create the Mix Category. For red and blue lines, the average was calculated from 100 replicates of 10-fold cross validation. The green line represents the performance of the final classifier in an independent gene set. Horizontal bars represent the standard deviations of recall. The dashed lines represent the standard deviation of precision for the blue curve.</p
This paper provides a comprehensive performance analysis of parametric and non-parametric machine le...
<p>The congruency index measured the similarity of word categorization from online to offline catego...
<p>For the solid blue and dashed black line, we predicted the annotations of single pattern and mixe...
<p>(A) Classification accuracy when the number of channels was reduced. The bold black, red, and blu...
<p>(<b>a</b>) Prediction performance of the classifier at individual level (PAM; P<i>i</i> indicates...
<p>(left) Mean accuracy for each speech category. Error bars show the standard error of the mean. Th...
<p>Precision-Recall curve for one and two object cases using for ensemble classifiers trained on exp...
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
Binary classifiers are routinely evaluated with performance measures such as sensitivity and specifi...
<p>When both features were used (red bars), approximately 96% of all stimuli were captured correctly...
<p>Semantic category-wise comparative analysis of recall with state-of-the-art CBIR techniques on th...
<p>Average classes' depths for the fifty simulated ontologies, compared to the value for the March 2...
<p><b>(A)</b> Receiver operating characteristic (ROC) curve. The solid black line indicates the medi...
<p>Average classes' heights for the fifty simulated ontologies, compared to the value for the March ...
(A), (B), (C), (D), and (E) display the PRCs for the logistic regression (LR), support vector machin...
This paper provides a comprehensive performance analysis of parametric and non-parametric machine le...
<p>The congruency index measured the similarity of word categorization from online to offline catego...
<p>For the solid blue and dashed black line, we predicted the annotations of single pattern and mixe...
<p>(A) Classification accuracy when the number of channels was reduced. The bold black, red, and blu...
<p>(<b>a</b>) Prediction performance of the classifier at individual level (PAM; P<i>i</i> indicates...
<p>(left) Mean accuracy for each speech category. Error bars show the standard error of the mean. Th...
<p>Precision-Recall curve for one and two object cases using for ensemble classifiers trained on exp...
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
Binary classifiers are routinely evaluated with performance measures such as sensitivity and specifi...
<p>When both features were used (red bars), approximately 96% of all stimuli were captured correctly...
<p>Semantic category-wise comparative analysis of recall with state-of-the-art CBIR techniques on th...
<p>Average classes' depths for the fifty simulated ontologies, compared to the value for the March 2...
<p><b>(A)</b> Receiver operating characteristic (ROC) curve. The solid black line indicates the medi...
<p>Average classes' heights for the fifty simulated ontologies, compared to the value for the March ...
(A), (B), (C), (D), and (E) display the PRCs for the logistic regression (LR), support vector machin...
This paper provides a comprehensive performance analysis of parametric and non-parametric machine le...
<p>The congruency index measured the similarity of word categorization from online to offline catego...
<p>For the solid blue and dashed black line, we predicted the annotations of single pattern and mixe...