<p>The best accuracy among all for each algorithm and 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>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
The effectiveness of ensembling for improving classification performance is well documented. Broadly...
This dissertation is about classification methods and class probability prediction. It can be roughl...
<p>The best accuracy among all for each algorithm and each repetition is written in bold and the wo...
<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>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The classification accuracies were averaged ov...
<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>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The influence of overlapped partitioning were ...
<p>The reported values are <i>mean</i> ± <i>standard deviation</i>, where the variation is the resul...
Ensemble learning schemes such as AdaBoost and Bagging enhance the performance of a single clas-sifi...
Our hypothesis is that building ensembles of small sets of strong classifiers constructed with diffe...
<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP...
<p>Levels of accuracy were calculated by 10-fold cross-validation and expressed as the means ± 10-fo...
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
The effectiveness of ensembling for improving classification performance is well documented. Broadly...
This dissertation is about classification methods and class probability prediction. It can be roughl...
<p>The best accuracy among all for each algorithm and each repetition is written in bold and the wo...
<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>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The classification accuracies were averaged ov...
<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>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The influence of overlapped partitioning were ...
<p>The reported values are <i>mean</i> ± <i>standard deviation</i>, where the variation is the resul...
Ensemble learning schemes such as AdaBoost and Bagging enhance the performance of a single clas-sifi...
Our hypothesis is that building ensembles of small sets of strong classifiers constructed with diffe...
<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP...
<p>Levels of accuracy were calculated by 10-fold cross-validation and expressed as the means ± 10-fo...
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
The effectiveness of ensembling for improving classification performance is well documented. Broadly...
This dissertation is about classification methods and class probability prediction. It can be roughl...
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