1. In a computer-based system, a method of training a multi-category classifier using a binary SVM algorithm, said method comprising: calculating at least one feature vector for each of a plurality of training examples; transforming each of said at least one feature vectors using a first mathematical function so as to provide desired information about each of said training examples; building a SVM classifier for each one of a plurality of categories, calculating a solution for the SVM classifier for the first category using predetermined initial value(s) for said at least one tunable parameter; and testing said solution for said first category to determine if the solution is characterized by either over-generalization or over-memorization, wherein the SVM classifier is used on real world data, the probability of category membership of the real world data being output to at least one of a user, another system, and another process, wherein whether said SVM classifier solution for said first category is characterized by either over-generalization or over-memorization is based on a difference between a harmonic mean of said first and second estimated probabilities, on the one hand, and an arithmetic mean of said first and second estimated probabilities, on the other hand.