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Abstract: A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased, but can be highly variable. This article discusses bootstrap estimates of prediction error, which can be thought of as smoothed versions of cross-validation. A particular bootstrap method, the 632+ rule, is shown to substantially... (Update)
Context of citations to this paper: More
.... 632 rule, a recently proposed improvement of the bootstrap method for assigning measures of accuracy to classification error estimates [3, 4]. The novel bootstrap rule had been successfully applied to develop non overfitting compact tree models in this medical and veterinary...
...may result in insu#cient training data for constructing the classifier. In this case, a method called leave one alone cross validation [65, 66] is used. All but one data item are used to build a classifier and the last one is withheld as testing data. Then it is repeated in a...
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BibTeX entry: (Update)
B. Efron and R. Tibshirani. Cross-validation and the bootstrap: Estimating the error rate of a prediction rule. 1995. Technical Report (TR-477), Dept. of Statistics, Stanford University. http://citeseer.ist.psu.edu/47726.html More
@misc{ efron95crossvalidation,
author = "B. Efron and R. Tibshirani",
title = "Cross-validation and the bootstrap: Estimating the error rate of a prediction
rule",
text = "B. Efron and R. Tibshirani. Cross-validation and the bootstrap: Estimating
the error rate of a prediction rule. 1995. Technical Report (TR-477), Dept.
of Statistics, Stanford University.",
year = "1995",
url = "citeseer.ist.psu.edu/47726.html" }
Citations (may not include all citations):1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
657 Bagging predictors - Breiman - 1994
546 An Introduction to the Bootstrap (context) - Efron, Tibshirani - 1993
258 Cross-validatory choice and assessment of statistical predic.. (context) - Stone - 1974
108 Bootstrap methods: another look at the jackknife (context) - Efron - 1979
107 Some comments on Cp (context) - Mallows - 1973
106 Discriminant Analysis and Statistical Pattern Recognition (context) - McLachlan - 1992
28 The predictive sample reuse method with applications (context) - Geisser - 1975
22 Cross-Validation and the Bootstrap: Estimating the Error Rat.. - Efron, Tibshirani - 1995
10 Bootstrap techniques for error estimation (context) - Jain, Dubes et al. - 1987
8 Training sequence size and vector quantizer performance (context) - Cosman, Perlmutter et al. - 1991
7 Submodel selection and evaluation in regression: the x-rando.. (context) - Breiman, Spector - 1992
6 Flexible metric nearest neighbour classification (context) - Friedman - 1994
5 Jackknife-after-bootstrap standard errors and influence func.. (context) - Efron - 1992
3 Spline bases, regularization, and generalized cross-validati.. (context) - Wahba - 1980
1 A study of cross-validation and bootstrap for accuracy asses.. (context) - Kohavi - 1995
1 Efficient bootstrap simulations (context) - Dawid, Hinkley et al. - 1986
1 Correction note to Application of bootstrap and other esampl.. (context) - Chernick, Murthy et al. - 1986
1 Application of bootstrap and other resampling methods: evalu.. (context) - Chernick, Murthy et al. - 1985
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