Patent ID: 7386527

Claim:
In a computer-based system, a method of training a multi-category classifier using a binary SVM algorithm, said method comprising: storing a plurality of user-defined categories in a memory of a computer; analyzing a plurality of training examples for each category so as to identify one or more features associated with each category; calculating at least one feature vector for each of said 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; and building a SVM classifier for each one of said plurality of categories, 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, wherein if any one of said examples belongs to both said first category and another category, such examples are assigned to the first class only; 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; 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 the test to determine 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 first and second estimated probabilities on the one hand, and an arithmetic mean of said first and second estimated probabilities on the other hand; wherein the first estimated probability is indicative of class membership and the second estimated probability is indicative of non-class membership for training examples.