Fig. (6).

Schematic view of SVM classifier: At the first step, using a function that is called “kernel function” (and is denoted by Ф) the protein pairs are transformed into points in a new space (presumably making the classification easier in this space). Then, the best separating hyperplane (separating line in this figure) is selected as the boundary of two classes, in this example each three thin black lines and the thick green line are separating lines. The margin for a separating hyperplane is the shortest distance from that hyperplane to the closest positive or negative example; in this figure the margin of thick green line is denoted by ‘w’. The best separating hyperplane is the one with the maximum margin; in this example the thick green line is the best separating line (closest points to the best separating hyperplane are called support vectors, the support vectors are circled in the figure).