K-SVCR.: A multi-class support vector machine

Support Vector Machines for pattern recognition are addressed to binary classification problems. The problem of multi-class classification is typically solved by the combination of a-class decision functions using voting scheme methods or decison trees. We present a new multi-class classification SVM for the separable case, called K-SVCR. Learning machines operating in a kernel-induced feature space are constructed assigning output +1 or -1 if training patterns belongs to the classes to be separated, and assigning output 0 if patterns have a different label to the formers. This formulation of multi-class classification problem ever assigns a meaningful answer to every input and its architecture is more fault-tolerant than standard methods one.