Abstract
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.
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Angulo, C., Català, A. (2000). K-SVCR. A Multi-class Support Vector Machine. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_4
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DOI: https://doi.org/10.1007/3-540-45164-1_4
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