Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
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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 multiclassification relies are not independent from each other and using a puncturing mechanism this dependence can be pruned, obtaining much better multiclassification schemes as shown by the carried out experiments.