Abstract
A novel approach to calculate the generalization error of the support vector machines anda new support vector machine—nonsymmatic support vector machine—is proposedhere. Our results are based upon the extreme value theory and both the mean andv ariance of the generalization error are exactly ontained.
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Feng, J. (2001). Non-symmetric Support Vector Machines. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_49
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DOI: https://doi.org/10.1007/3-540-45720-8_49
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