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An example of the distribution of features used in present work is shown in Figure 6 for detections of strong (a) and weak (b) signals.
The distribution of features such as forage quality, water, or shade within a pasture can change the behavior of grazers and thus, the impact of their grazing.
In [16], the authors proposed a vehicle model recognition system based on a SIFT of an image of the vehicle's headlights and the homogeneity, which were calculated based on the distribution of features.
Considering other techniques like whitening pre-processing can affect the distribution of features activations, we can draw the inference that whitening transformation indirectly influences the selection of pooling methods.
In these studies, the distribution of features such as vesicle pool sizes or release probabilities was typically skewed positively with a predominance of lower values [6], [9], [10], [18], [19] and in some cases approximated by a Γ density function [9], [10].
Table 1 shows the distribution of features.
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Figures 6 and 7 indicate the distribution of feature values of labeled points.
And the distribution of feature maps after convolution is naturally different from that without whitening processing.
Figure 5 is the distribution of Feature 8 after mapping process.
To this end, NB must estimate the distribution of feature values.
From Fig. 2, the distribution of feature where entropy acts the communication scheme on the 11 SNRs is shown.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com