Abstract
In this paper, we propose a multi-category classification method that combines binary classifiers through soft-max function. Posteriori probabilities are also obtained. Both, one-versus-all and one-versus- one classifiers can be used in the combination. Empirical comparison shows that the proposed method is competitive with other implementations of one-versus-all and one-versus-one methods in terms of both classification accuracy and posteriori probability estimate.
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© 2003 Springer-Verlag Berlin Heidelberg
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Duan, K., Keerthi, S.S., Chu, W., Shevade, S.K., Poo, A.N. (2003). Multi-category Classification by Soft-Max Combination of Binary Classifiers. In: Windeatt, T., Roli, F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44938-8_13
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DOI: https://doi.org/10.1007/3-540-44938-8_13
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