Title
Multicategory Support Vector Machines
Speaker
Yoonkyung Lee, The Ohio State University
Abstract
The Support Vector Machine (SVM) has become a popular choice of classification tool in practice. Despite its theoretical properties and its empirical success in solving binary problems, generalization of the SVM to more than two classes has not been obvious. Oftentimes multicategory problems have been treated as a series of binary problems in the SVM paradigm. However, solutions to a series of binary problems may not be optimal for the original multicategory problem. We propose multicategory SVMs, which extend the binary SVM to the multicategory case, and encompass the binary SVM as a special case. The proposed method deals with the equal misclassification cost and the unequal cost case in a unified way. It is shown that the multicategory SVM implements the optimal classification rule for appropriately chosen tuning parameters as the sample size gets large. The effectiveness of the method is demonstrated through simulation studies and real applications to cancer classification problems using gene expression data.
