<p>The best mean accuracy among all for each repetition is written in bold and the worst is underlined. An overlapped ensemble classifier becomes an ensemble classifier with naive partitioning when and . The classifier is equivalent to a single classifier when and .</p
<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP...
The generalization error, or probability of misclassification, of ensemble classifiers has been show...
Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more acc...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best accuracy among all for each algorithm and each repetition is written in bold and the wo...
<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The influence of overlapped partitioning were ...
<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The classification accuracies were averaged ov...
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
This dissertation is about classification methods and class probability prediction. It can be roughl...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
<p>Training data were first divided into five blocks. Assuming that those five blocks were aligned a...
Class noise, as know as the mislabeled data in training set, can lead to poor accuracy in classifica...
Popular ensemble classifier induction algorithms, such as bagging and boosting, construct the ensemb...
<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP...
The generalization error, or probability of misclassification, of ensemble classifiers has been show...
Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more acc...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best mean accuracy among all for each repetition is written in bold and the worst is underli...
<p>The best accuracy among all for each algorithm and each repetition is written in bold and the wo...
<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The influence of overlapped partitioning were ...
<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The classification accuracies were averaged ov...
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
This dissertation is about classification methods and class probability prediction. It can be roughl...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
<p>Training data were first divided into five blocks. Assuming that those five blocks were aligned a...
Class noise, as know as the mislabeled data in training set, can lead to poor accuracy in classifica...
Popular ensemble classifier induction algorithms, such as bagging and boosting, construct the ensemb...
<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP...
The generalization error, or probability of misclassification, of ensemble classifiers has been show...
Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more acc...
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