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The classification functions can be used to assign each case to the most likely group.
Based on the derived discriminant functions, classification functions can be computed.
In this work, the classification functions were derived from the linear discrimination functions.
The classification functions obtained are given in Table 5 and the classification matrix is given in Table 6.
These classification functions were then applied to find new lead antitrichomonal agents and six compounds were selected as possible active compounds by computational screening.
We conducted supervised learning over our annotated posts using a variety of classification functions: naive Bayes, logistic regression, classification trees, boosting, and support vector machines (SVM).
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These patterns, called support vectors, finally define the classification function.
The classification function can be defined in several ways.
A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed.
Afterwards, a linear classification function was derived allowing the discrimination between active and inactive compounds.
SVM has high generalization capability because it applies a maximum-margin classification function.
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