Abstract
This chapter is devoted to the same problems as Chapter 3, but for the situations where no parametric model of probability distributions is known and nonparametric decision rules (Rosenblatt-Parzen, k-nearest neighbor) are used for classification. We find optimal values for smoothness parameters that optimize the robustness factor. We compare stability of parametric and nonparametric decision rules.
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© 1996 Springer Science+Business Media Dordrecht
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Kharin, Y. (1996). Robustness of Nonparametric Decision Rules and Small-sample Effects. In: Robustness in Statistical Pattern Recognition. Mathematics and Its Applications, vol 380. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8630-6_4
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DOI: https://doi.org/10.1007/978-94-015-8630-6_4
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4760-1
Online ISBN: 978-94-015-8630-6
eBook Packages: Springer Book Archive