Abstract
SVMs were primarily proposed to deal with binary classification. In this work an alternative O(log2(n)) method for multiple classes classification using SVMs is proposed. Experimental results showed that it can be 23 times faster than the one vs one method, and 1.3 times faster than the one vs all classic methods, with the same error rate. Tests were performed on a speaker independent, isolated word speech recognition scenario.
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Coelho, S.T., Ynoguti, C.A. (2010). A Histogram Based Method for Multiclass Classification Using SVMs. In: Hussain, A., Aleksander, I., Smith, L., Barros, A., Chrisley, R., Cutsuridis, V. (eds) Brain Inspired Cognitive Systems 2008. Advances in Experimental Medicine and Biology, vol 657. Springer, New York, NY. https://doi.org/10.1007/978-0-387-79100-5_13
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