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Based on this vector, the classifier will classify the file as either benign or malicious.
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In this paper, we proposed three SVM classifiers: the non-classified normalization classifier, classified normalization classifier, and combination classifier with the above two classifiers, as shown in Table 2.
The order of the rules in the classifier is important and in classifying a case, the first rule that satisfies it will classify it.
In such a situation, all subwindows located on the lower part of the face will overlap the scarf, and thus all associated weak classifiers will tend to classify these subwindows as non-face.
Each binary classifier will determine whether the sample should be classified as one of the two classes.
The goal of learning is to estimate a classifier, which will correctly classify unseen examples.
A nonlinear classifier can classify linearly inseparable samples, nevertheless the time complexity of the nonlinear classifier will be increased when processing linearly separable samples.
A perfect classifier will have AUC = 1 and a random classifier will have AUC = 0.5.
A binary classifier classifies elements of a given test set into only two groups.
We noticed that this classifier classifies all instances as having a relation.
The classifier classified a sentence to either "section heading" or "section heading with text" or "text".
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com