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. 14. The method of claim 1 wherein said process of building a SVM classifier comprises: assigning each of said examples in a first category to a first class and all other examples belonging to other categories to a second class; optimizing at least one tunable parameter of a SVM classifier for said first categories, wherein said SVM classifier is trained using said first and second classes after the at least one tunable parameter has been optimized; and optimizing a second mathematical function that converts the output of the binary SVM classifier into a probability of category membership number of examples in said first category and dividing by the number of examples in all categories.