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.
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. 3. The method of claim 1 further comprising testing whether the trained SVM classifier could be optimized, wherein if said SVM classifier could not be optimized, said SVM classifier for said first category is discarded.