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Based on a reference collection of characterization scores assigned by diverse life scientists, we trained a classification function to predict scores depending on functional annotations in the Entrez Gene database.
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.
Illustration of an SVC classification function represented by w T x.
A high quality surrogate is achieved using a probabilistic classification function together with a metric for refinement of the model.
Comparisons are made using three benchmark case studies, one each from classification, function approximation and prediction domains.
Finally, support vector machines (SVM) are used to detect instead of using the classification function of CNN itself.
This is the first study that used a discriminant classification function of modern dung to identify coprolites.
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