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In the characterization experiments the features produced a result of approximately 89% correct classification.
That means it's possible that – if all goes well with the new app experiments – the features they offer could eventually make their way to Google Photos, as well.
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In the following experiments, the feature fusion extraction method is adopted.
For resequencing experiments, the feature coverage analysis enables exploration of the set of common or divergent features between the reference and its tracks and among the tracks (Supplementary Fig. S9).
While the feedback application itself stayed the same for the whole experiment, the features on which the classifier operated and the adaptation methods changed from level to level as described below.
In the presented experiments these features are the height, the spherical variance, and the gradient of the surface.
Numerical experiments illuminate the features of the method.
Experiments show the features of interfaces, and prove that the proposed prototype has the best interoperability to other typical stores.
For each experiment, the feature selection is performed using CFS to the training set of each fold.
Analyzing some examples of our own experiments, the essential features needed for modeling cell/material interaction studies are presented.
From (a) −(d) in Fig. 1, and a large number of other experiments, the intrinsic features of EMV could be summarized as follows.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com