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Color histograms in ROIs were extracted and classified by a trained classifier.
Discriminant value for a testing instance x given by a trained classifier is: f(x) = ∑ i α i y iK x,x i) + b.
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In wrapper type methods, the FS is "wrapped" around a learning method: the usefulness of a feature subset is directly judged by the estimated accuracy of a trained classifier.
The reduced vectors were classified by the trained classifier.
Remaining points are classified by the trained classifier.
As discussed above, different ways of partitioning the original dataset into training and test sets may lead to (somewhat) different performance by the trained classifier.
On the basis of these results, we conclude that the number of genes falsely predicted to be PID genes by the trained classifier is ∼2%.
Four features were selected by the trained classifier: MinCDF, Energy, AVE, and MaxF (COOC); percent mammographic density was not selected by the trained classifier but was forced into the models where noted.
The probability of classification can be interpreted as a measure of meditation ability by using the trained classifier to predict class membership in novice meditators.
The confidence score for each hyperlink (i.e., score l ) is obtained by applying the trained classifier on its feature vector.
The premise of this strategy is that the intrinsically important features will always be chosen by the best trained classifier, which should be independent of the specific sampling.
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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