A Note on the bias in SVMs for multiclassification.
During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance.
Gonzalez-Abril
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Department of Applied Economics I, University of Seville, Seville, Spain. luisgon@us.es
Angulo
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Velasco
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Ortega
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D015982:Bias; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted