Multicategory Support Vector Machines

[Grace Wahba]

   The following short paper is available at 
  http://www.stat.wisc.edu/~wahba/trindex.html

      Multicategory Support Vector Machines 
           (Preliminary Long Abstract)
      Yoonkyung Lee, Yi Lin and Grace Wahba 
 University of Wisconsin-Madison Statistics Dept, TR 1040
                 
		 Abstract

Support Vector Machines (SVMs) have shown great performance
in practice as a classification methodology recently. Even though
the SVM implements the optimal classification rule asymptotically
in the binary case, the one-versus-rest approach to solve 
the multicategory case using an SVM is not optimal.
We have proposed Multicategory SVMs, which extend the binary SVM to
the multicategory case, and encompass the binary SVM as a special case.
The Multicategory SVM implements the optimal classification rule 
as the sample size gets large, overcoming the suboptimality of
conventional one-versus-rest approach. The proposed method deals  
with the equal misclassification cost and the unequal cost case
in unified way.