<p>To assess the robustness of the proposed classification scheme, two-fold cross-validation experiments have been performed, where we measured the change in classification error after interchanging the training and test sets. (A) Cumulative distribution of the error difference for the USA dataset. (B) Cumulative distribution of the error difference for the Netherlands dataset.</p
Cross-validation, an economical method for assessing whether sample results will generalize, is disc...
<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. Then the data set A was classified by those c...
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>We evaluated the robustness of our classification algorithms by testing with different sizes for ...
In the machine learning field the performance of a classifier is usually measured in terms of predic...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
Cross-validation is one of the most widely used techniques, in estimating the Generalization Error o...
Abstract Background To estimate a classifier’s error in predicting future observations, bootstrap me...
Abstract — Cross-validation is a very commonly employed technique used to evaluate classifier perfor...
<p>The table shows the cross-validation performance of our method on the labeled data points. The me...
<p>We show the averaged results of a 10-fold cross validation experiment on . For each , we plot the...
<p>The correct rates (%) were derived with systematically varying number of labels (L), number of sa...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
Data that have not been modeled cannot be correctly predicted. Under this assumption, this research ...
Cross-validation, an economical method for assessing whether sample results will generalize, is disc...
<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. Then the data set A was classified by those c...
<p>For most classifiers, cross-validation is used at two levels: at an outer level for training and ...
<p>We evaluated the robustness of our classification algorithms by testing with different sizes for ...
In the machine learning field the performance of a classifier is usually measured in terms of predic...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
Cross-validation is one of the most widely used techniques, in estimating the Generalization Error o...
Abstract Background To estimate a classifier’s error in predicting future observations, bootstrap me...
Abstract — Cross-validation is a very commonly employed technique used to evaluate classifier perfor...
<p>The table shows the cross-validation performance of our method on the labeled data points. The me...
<p>We show the averaged results of a 10-fold cross validation experiment on . For each , we plot the...
<p>The correct rates (%) were derived with systematically varying number of labels (L), number of sa...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
Data that have not been modeled cannot be correctly predicted. Under this assumption, this research ...
Cross-validation, an economical method for assessing whether sample results will generalize, is disc...
<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. Then the data set A was classified by those c...