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 2-class decision functions using voting scheme methods or decison trees. We present a new multi-class classification SVM for the separable case, called KSVCR. 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.
CITATION STYLE
Angulo, C., & Català, A. (2000). K-SVCR. A multi-class support vector machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1810, pp. 31–38). Springer Verlag. https://doi.org/10.1007/3-540-45164-1_4
Mendeley helps you to discover research relevant for your work.