The consistency of multicategory support vector machines

The goal of classification (or pattern recognition) is to construct a classifier with small misclassification error. The notions of consistency and universal consistency are important to the construction of classification rules. A consistent rule guarantees us that taking more samples essentially suffices to roughly reconstruct the unknown distribution. Support vector machine (SVM) algorithm is one of the most important rules in two category classification. How to effectively extend the SVM for multicategory classification is still an on-going research issue. Different versions of multicategory support vector machines (MSVMs) have been proposed and used in practice. We study the one designed by Lee, Lin and Wahba with hinge loss functional. The consistency of MSVMs is established under a mild condition. As a corollary, the universal consistency holds true if the reproducing kernel Hilbert space is dense in C norm. In addition, an example is given to demonstrate the main results.