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A reason for this was the focus on evaluating our feature set against the same data, only then we can make a comparison between two feature sets or a set of different feature sets.
The consistency index between two feature sets S and is defined as (Details can be found in the Supplementary Materials).
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Once all possible matches between the two feature sets FP a and FP b have been computed, we need to skim the matches set from its original size FP a · FP b to a more treatable dimension.
We selected only distinct features from these two feature sets to build PromoBot.
See text for description of the four feature sets.
Two features set this approach apart from that taken here.
An observed correlation between two features that is caused by direct relationships between these two features and a third feature.
We then proceeded to compare three different feature sets.
Fig. 1 Comparison between different feature sets of the four classical machine learning methods, based on mean AUCs and mean ACCs of the 10 times 10-fold cross-validation.
In such a way, no correlation can be exploited between feature sets protected by two (or more) related instances of the applied fuzzy vault records.
Regardless of feature point type, FB matching methods automatically find correspondences between two sets of features.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com