<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The influence of overlapped partitioning were evaluated by changing the degree of overlaps () and the number of intensifications (). The classification performances of all participants were presented.</p
<p>Training data were first divided into five blocks. Assuming that those five blocks were aligned a...
The main purpose of this study was to determine whether it is possible to somehow use results on tra...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. Then the data set A was classified by those c...
<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The classification accuracies were averaged ov...
<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>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>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>We analyzed two P300-based BCI data sets A and B respectively. Data set A was recorded in this on...
Dynamic ensemble learning methods explore the use of different classifiers for different samples, th...
Abstract — Cross-validation is a very commonly employed technique used to evaluate classifier perfor...
This paper presents a novel cluster oriented ensemble classifier. The proposed ensemble classifier i...
<p>Training data were first divided into five blocks. Assuming that those five blocks were aligned a...
The main purpose of this study was to determine whether it is possible to somehow use results on tra...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
<p>OSWLDA, OPCALDA and OLDA were trained on 8100 ERPs. Then the data set A was classified by those c...
<p>OSWLDA, OPCALDA and OLDA were trained on 900 ERPs. The classification accuracies were averaged ov...
<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>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>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>We analyzed two P300-based BCI data sets A and B respectively. Data set A was recorded in this on...
Dynamic ensemble learning methods explore the use of different classifiers for different samples, th...
Abstract — Cross-validation is a very commonly employed technique used to evaluate classifier perfor...
This paper presents a novel cluster oriented ensemble classifier. The proposed ensemble classifier i...
<p>Training data were first divided into five blocks. Assuming that those five blocks were aligned a...
The main purpose of this study was to determine whether it is possible to somehow use results on tra...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
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