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For the scenario 2, using the 1D methodology (methods 1 to 4), the EEG data needed a small amount of features to achieve high performance rates.
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Since it has only a small amount of feature vectors available, this method is dependent on the data exchange with its neighbors.
However, only a small amount of IT features are initially selected (most notably Markov features; Additional file 1: Table S1) while other IT features are included later during feature selection (e.g., MI_CG and Shannon; Additional file 1: Table S1).
This behaviour was confirmed by the fractographical examination that showed an inter-splat fracture surface at room temperature with small amount of plastic features, but at 800 °C a more dimpled and ruptured fracture appeared.
It has been shown, for example, in [9] that the feature space dimensionality in which the considered classifier is trained can have a significant impact on its performances: a too small amount of images regarding dimensionality (the number of features) might lead to an improper training of the classifier and thus to results with a possibly high statistical variance.
The AGU requires only a small amount of data which characterizes the feature of H.
The latter signal contained features characteristic of a small amount of NO radical being trapped in the frozen matrix solution.
With only a small amount of refinement, the computer-to-phone feature could be a perfectly satisfactory replacement for ordinary long-distance calling.
Because of this, the size of the database was very limited in size, since only a small amount of subjects had information on all the features being analyzed.
The constraint score approach selects features based on a small amount of labeled data but ignores unlabeled data.
Wear a small amount of makeup so you can enhance your natural features instead of masking them.
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CEO of Professional Science Editing for Scientists @ prosciediting.com