Effective multi-class support vector machine classification
First Claim
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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Abstract
An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm. In one preferred embodiment, the method includes the following steps: 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 the examples; transforming each of the at least one feature vectors so as reflect information about all of the training examples; and building a SVM classifier for each one of the plurality of categories, wherein the process of building a SVM classifier further includes: assigning each of the examples in a first category to a first class and all other examples belonging to other categories to a second class, wherein if anyone of the examples belongs to another category as well as the first category, such examples are assigned to the first class only, optimizing at least one tunable parameter of a SVM classifier for the first category, wherein the SVM classifier is trained using the first and second classes; and optimizing a function that converts the output of the binary SVM classifier into a probability of category membership.
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Citations
16 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer-readable medium storing instructions that when executed by a computer cause the computer to perform the following steps:
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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; and building a SVM classifier for each one of said 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. - View Dependent Claims (16)
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Specification