Abstract
Estimating the error probability L n = P{g n (X) ≠ Y|D n } of a classification function g n is of essential importance. The designer always wants to know what performance can be expected from a classifier. As the designer does not know the distribution of the data—otherwise there would not be any need to design a classifier—it is important to find error estimation methods that work well without any condition on the distribution of (X, Y). This motivates us to search for distribution-free performance bounds for error estimation methods.
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© 1996 Springer Science+Business Media New York
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Devroye, L., Györfi, L., Lugosi, G. (1996). Error Estimation. In: A Probabilistic Theory of Pattern Recognition. Stochastic Modelling and Applied Probability, vol 31. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0711-5_8
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DOI: https://doi.org/10.1007/978-1-4612-0711-5_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6877-2
Online ISBN: 978-1-4612-0711-5
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