<p>(a) semantic verbal fluency, (b) phonological verbal fluency and (c) combined datasets. Classification performance was significantly different between all algorithms and significantly higher for each algorithm with the combined dataset (<i>p</i> < 0.001, u-test).</p
<p>Comparison results of classification performance among ELM, SVM, and BPNN.</p
<p>AR: Accuracy rate, SE: Sensitivity, SP: Specificity, PPV: Positive predictive value, NPV: Negativ...
This paper is an empirical study on the performance of different discriminative approaches to rerank...
Seven classifiers are compared on sixteen quite different, standard and extensively used datasets in...
Performance of Support Vector Machine, Random Forest, Multilayer perception, and XGBoost classifiers...
Performance of Support Vector Machine, Random Forest, Multilayer perception, and XGBoost classifiers...
<p>The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...
This thesis is a critical empirical study, using a range of benchmark datasets, on the performance o...
We present results from a large-scale empirical comparison between ten learning methods: SVMs, neur...
<p>SVM model is tested by three different datasets, only genotype, only phenotype and integrated phe...
In the field of machine learning classification is one of the most common types to be deployed in so...
<p>The classification performance (ARR) of SVM with the real best kernel vs. with the recommended ke...
<p>Comparison of the performance of Support Vector Machine (SVM) classifier with sampling using poly...
. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thir...
<p>The middle column indicates the mean of the accuracy scores for the 10 fold cross validation expe...
<p>Comparison results of classification performance among ELM, SVM, and BPNN.</p
<p>AR: Accuracy rate, SE: Sensitivity, SP: Specificity, PPV: Positive predictive value, NPV: Negativ...
This paper is an empirical study on the performance of different discriminative approaches to rerank...
Seven classifiers are compared on sixteen quite different, standard and extensively used datasets in...
Performance of Support Vector Machine, Random Forest, Multilayer perception, and XGBoost classifiers...
Performance of Support Vector Machine, Random Forest, Multilayer perception, and XGBoost classifiers...
<p>The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...
This thesis is a critical empirical study, using a range of benchmark datasets, on the performance o...
We present results from a large-scale empirical comparison between ten learning methods: SVMs, neur...
<p>SVM model is tested by three different datasets, only genotype, only phenotype and integrated phe...
In the field of machine learning classification is one of the most common types to be deployed in so...
<p>The classification performance (ARR) of SVM with the real best kernel vs. with the recommended ke...
<p>Comparison of the performance of Support Vector Machine (SVM) classifier with sampling using poly...
. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thir...
<p>The middle column indicates the mean of the accuracy scores for the 10 fold cross validation expe...
<p>Comparison results of classification performance among ELM, SVM, and BPNN.</p
<p>AR: Accuracy rate, SE: Sensitivity, SP: Specificity, PPV: Positive predictive value, NPV: Negativ...
This paper is an empirical study on the performance of different discriminative approaches to rerank...