Abstract #300400

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JSM 2003 Abstract #300400
Activity Number: 105
Type: Invited
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #300400
Title: Multicategory Support Vector Machines
Author(s): Yoonkyung Lee*+
Companies: The Ohio State University
Address: Department of Statistics, Columbus, OH, 43210-1247,
Keywords: support vector machine ; machine learning ; classification
Abstract:

Support Vector Machines (SVM) have been a popular choice of classification methods in the machine learning community. It is well-known in the binary case that the SVM can be cast in a regularization framework with the hinge loss, which is a convex upper bound of the zero-one misclassification loss. Due to its specific mechanism, it may fail under a variety of circumstances to solve multicategory classification problems by a series of binary SVMs. We introduce the Multicategory Support Vector Machine (MSVM), an extension of the binary SVM to the multicategory case by employing an extended hinge loss, and highlight its good properties.

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