Abstract
In this chapter we investigate pattern recognition problems for which the hypothetical assumptions about training samples are disturbed in various ways: the class-i training sample is contaminated by elements from alien classes, or samples contain outliers, or elements of the training sample are statistically dependent. We estimate the robustness factor and analyze its dependence on sample sizes, distortion levels, and other factors. We construct new decision rules with higher order of robustness and illustrate their stability by computer results.
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© 1996 Springer Science+Business Media Dordrecht
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Kharin, Y. (1996). Decision Rule Robustness under Distortions of Training Samples. In: Robustness in Statistical Pattern Recognition. Mathematics and Its Applications, vol 380. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8630-6_6
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DOI: https://doi.org/10.1007/978-94-015-8630-6_6
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4760-1
Online ISBN: 978-94-015-8630-6
eBook Packages: Springer Book Archive