Figure 4.

Illustration of the difference between discriminant (A) and modeling (B) classification techniques. Discriminant classification techniques (A) divide the available hyperspace into as many regions as the number of the investigated categories (three, in the present example), so that whenever a sample falls in a particular region of space, it is always assigned to the associated class. Modeling techniques (B) build a separate model for each one of the categories of interest, so that there can be regions of spaces where more than a class is mapped and others where there is no class at all.