Puncturing multi-class Support Vector Machines

Abstract

Non-binary classification has been usually addressed by training several binary classification when using Support Vector Machines (SVMs), because its performance does not degrade compared to the multi-class SVM and it is simpler to train and implement. In this paper we show that the binary classifiers in which the multi-classification relies are not independent from each other and using a puncturing mechanism this dependence can be pruned, obtaining much better multi-classification schemes as shown by the carried out experiments. © Springer-Verlag Berlin Heidelberg 2002.