Abstract
The Support Vector Machine, introduced in [1] as a practical implementation of the principle of structural risk minimization, constitutes one of the most promising methods for constructing a mathematical model only on the base of a limited amount of measured data. In this paper, we consider the application of this method to the problem of nonparametric binary hypothesis testing (bayesian setting); the main contribution of this paper is the derivation of the Support Vector algorithm in the case of a generic convex approximation of the binary risk function.
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Mattera, D., Palmieri, F. (1999). Support Vector Machine for Nonparametric Binary Hypothesis Testing. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-98. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0811-5_11
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DOI: https://doi.org/10.1007/978-1-4471-0811-5_11
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1208-2
Online ISBN: 978-1-4471-0811-5
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