IEEE Transactions on Pattern Analysis and Machine Intelligence · 1988 · 144 citations · 110 references
ComputingComputer ScienceMathematicsAlgorithmsData MiningMachine LearningClassificationDecision TreesKernel MethodsNearest Neighbor SearchPattern RecognitionSequence Pattern RecognitionClassification AlgorithmsK-nearest Neighbor SearchK-nearest NeighborsMachine Learning ClassificationNearest-neighbor ClassificationRule-based ClassificationSmoothingSmoothing Parameter Selection
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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Classification and Regression Trees.
John Van Ryzin, Leo Breiman, Jerome H. Friedman et al. · Journal of the American Statistical Association · 1986 · 21K citations
Bootstrap Methods: Another Look at the Jackknife
B. Efron · The Annals of Statistics · 1979 · 17.4K citations · Full text
Nearest neighbor pattern classification
Thomas M. Cover, Peter E. Hart · IEEE Transactions on Information Theory · 1967 · 16.1K citations