Abstract
This chapter describes main probability models of observed data in pattern recognition: random variables, random vectors, random processes, random fields, and random sets. Optimal (Bayesian) decision rules minimizing the classification risk are specified. These decision rules are defined in discrete and continuous spaces of feature variables. The computational formulae for risk are given.
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© 1996 Springer Science+Business Media Dordrecht
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Kharin, Y. (1996). Probability Models of Data and Optimal Decision Rules. In: Robustness in Statistical Pattern Recognition. Mathematics and Its Applications, vol 380. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8630-6_1
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DOI: https://doi.org/10.1007/978-94-015-8630-6_1
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4760-1
Online ISBN: 978-94-015-8630-6
eBook Packages: Springer Book Archive