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Exact(47)
Required preprocessing and appropriate feature extraction were applied.
The appropriate feature is a type of unity or ordering of these states.
Moreover, different learning algorithms combined with appropriate feature selection methods may arrive at better performance.
For each metric we select the relevant benchmark to test the appropriate feature.
Signals represented in an appropriate feature space are used to train a learning algorithm.
Dimensionality reduction is performed next, to provide the classifier with the appropriate feature descriptors.
Similar(12)
Thus, selecting appropriate features affects clustering performance positively.
This makes it easier to define appropriate features.
Feature selection techniques attempt to determine appropriate features that can discriminate well between classes.
Table 1 shows the correspondence among facial events, appropriate features and default SPThres values.
Appropriate features are introduced which enable the use of supervised classification.
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