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Effective multi-class support vector machine classification

  • US 7,533,076 B2
  • Filed: 03/17/2008
  • Issued: 05/12/2009
  • Est. Priority Date: 12/06/2002
  • Status: Expired due to Term
First Claim
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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.

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