<p>Training data were first divided into five blocks. Assuming that those five blocks were aligned around a circle, three continuous blocks were selected to form a partition. As a result, five partitions were prepared. The partitioned training data sets were used to train weak learners in the ensemble classifier.</p
(a) Two chunks shared the last component “d” in a random input sequence. (b) Activities of two reado...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
The diversity of an ensemble of classifiers is known to be an important factor in determining its ge...
<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 ...
Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize loc...
<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>(A) Target classes: <i>P</i>—positive, <i>D</i>—negative. (B) Trained ensemble region on the plan...
There are several aspects that might influence the performance achieved by existing learning systems...
<p>The training dataset is classified by all base classifiers. After K-Means clustering and circulat...
<p>We analyzed two P300-based BCI data sets A and B respectively. Data set A was recorded in this on...
Many simulation data sets are so massive that they must be distributed among disk farms attached to ...
In the experiment of imbalanced problems, 50 imbalanced data sets from the Knowledge Extraction base...
(a) Two chunks shared the last component “d” in a random input sequence. (b) Activities of two reado...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
The diversity of an ensemble of classifiers is known to be an important factor in determining its ge...
<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 ...
Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize loc...
<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>(A) Target classes: <i>P</i>—positive, <i>D</i>—negative. (B) Trained ensemble region on the plan...
There are several aspects that might influence the performance achieved by existing learning systems...
<p>The training dataset is classified by all base classifiers. After K-Means clustering and circulat...
<p>We analyzed two P300-based BCI data sets A and B respectively. Data set A was recorded in this on...
Many simulation data sets are so massive that they must be distributed among disk farms attached to ...
In the experiment of imbalanced problems, 50 imbalanced data sets from the Knowledge Extraction base...
(a) Two chunks shared the last component “d” in a random input sequence. (b) Activities of two reado...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
The diversity of an ensemble of classifiers is known to be an important factor in determining its ge...
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