Abstract
In this chapter we select a decision rule from a class of rules with the help of training data. Working formally, let C be a class of functions Ø: R d → {0,1}. One wishes to select a function from C with small error probability. Assume that the training data D n = ((X 1, Y 1),..., (X n , Y n )) are given to pick one of the functions from C to be used as a classifier.
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© 1996 Springer Science+Business Media New York
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Devroye, L., Györfi, L., Lugosi, G. (1996). Vapnik-Chervonenkis Theory. 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_12
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DOI: https://doi.org/10.1007/978-1-4612-0711-5_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6877-2
Online ISBN: 978-1-4612-0711-5
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