Effective multi-class support vector machine classification

A method of classifying new examples into at least one of multiple categories, comprising:

• analyzing a new example so as to generate a feature vector for said new example;
• classifying the feature vector into a first category using a binary SVM classifier;
• transforming a SVM score calculated for said first category using a sigmoid function so as to generate at least one probability estimate;
• using a misclassification cost matrix to transform said at least one probability estimate;
• and
• determining whether any of said at least one transformed probability estimates are above a probability threshold.

A method of classifying new examples into at least one of multiple categories, comprising:

• analyzing a new example so as to generate a feature vector for said new example;
• classifying the feature vector into a first category using a binary SVM classifier;
• transforming a SVM score calculated for said first category using a sigmoid function so as to generate at least one probability estimate;
• using a category prior matrix to transform said at least one probability estimate;
• and
• determining whether any of said at least one transformed probability estimates are above a probability threshold.

A computer-readable medium for storing instructions that when executed by a computer perform a method of training a multi-category classifier using a binary SVM algorithm, the 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 category, wherein said SVM classifier is trained using said first and second classes;
• and
• optimizing a second mathematical function that converts the output of the binary SVM classifier into a probability of category membership.

A computer-readable medium containing computer-executable instructions that when executed by a computer perform a method of classifying new examples into at least one of multiple categories, the method comprising:

• analyzing a new example so as to generate a feature vector for said new example;
• classifying the feature vector into a first category using a binary SVM classifier;
• transforming a SVM score calculated for said first category using a sigmoid function so as to generate at least one probability estimate;
• using a misclassification cost matrix to transform said at least one probability estimate;
• and
• determining whether any of said at least one transformed probability estimates are above a probability threshold.

A computer-readable medium containing computer-executable instructions that when executed by a computer perform a method of classifying new examples into at least one of multiple categories, the method comprising:

• analyzing a new example so as to generate a feature vector for said new example;
• classifying the feature vector into a first category using a binary SVM classifier;
• transforming a SVM score calculated for said first category using a sigmoid function so as to generate at least one probability estimate;
• using a category prior matrix to transform said at least one probability estimate;
• and
• determining whether any of said at least one transformed probability estimates are above a probability threshold.