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The best classification systems incorporate objective physical characteristics of both composition and texture as well as mode of genesis.
This combination showed the best classification performance.
EEG channels that offer the best classification performance were determined.
Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).
The best classification results for the 1st level were 89.5%, whereas for the 2nd level 90.1%.
However, considering all channels provide the best classification performance irrespective of the task or the subject.
Secondly, the best classification performances were returned by the SNB approach for all three data sets.
For SVMs, we find that the RBF kernel with γ=0.15 provides the best classification results.
Table 2 shows the best classification result for the skating dataset with shape context features.
Figure 8 illustrates the receiver operating characteristic (ROC) curve of the best classification system [32].
It can be seen that with LE dimension reduction, the best classification result is obtained.
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