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Each unique subject-session pair in our dataset is an "observed sample" that is described by its feature vector.
We use the SVM classifier to decide in which class each head image (characterized by its feature vector) is related.
In their model, a behavior i belonging to an attack class j is represented by the variable x ij and each class is represented by its feature vector as F j.
We will refer to each contig by its feature vector X = (x 1, x 2 ), where x 1 is coverage and x 2 is read depth.
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Each potential lesion region is classified by feeding its feature vector as the input to the SVM classifier.
Once the model is trained, to predict if a passenger will attain silver status in a given time frame S (in the future) we only need to generate its feature vector by observing the passenger for a period of time D since their first flight.
The confidence score for each hyperlink (i.e., score l ) is obtained by applying the trained classifier on its feature vector.
NSC represents each class by its centroid (mean feature vector) and classifies new instances by assigning them the class of the closest centroid.
With all samples represented by a feature vector, now it is possible for us to construct our predictor using the machine learning approach.
These two transforms are conducted by multiplying the feature vector by linear transform matrices.
We reconstruct a new data set by a feature vector with larger eigenvalues according to PCA.
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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