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<p>A test sequence is used to select the best rule from a class of discrimination rules defined in terms of the training sequence. The Vapnik-Chervonenkis and related inequalities are used to obtain distribution-free bounds on the difference between the probability of error of the selected rule and the probability of error of the best rule in the given class. The bounds are used to prove the consistency and asymptotic optimality for several popular classes, including linear discriminators, nearest-neighbor rules, kernel-based rules, histogram rules, binary tree classifiers, and Fourier series classifiers. In particular, the method can be used to choose the smoothing parameter in kernel-based rules, to choose k in the k-nearest neighbor rule, and to choose between parametric and nonparametric rules.</p>
automatic pattern recognition; error statistics; artificial intelligence; probability; training sequence; linear discriminators; nearest-neighbor rules; kernel-based rules; histogram rules; binary tree classifiers; Fourier series classifiers; artificial intelligence; computerised pattern recognition; error statistics; probability

L. Devroye, "Automatic Pattern Recognition: A Study of the Probability of Error," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 10, no. , pp. 530-543, 1988.
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