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On Practical Choice of Smoothing Parameter in Nonparametric Classification
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 Title & Authors
On Practical Choice of Smoothing Parameter in Nonparametric Classification
Kim, Rae-Sang; Kang, Kee-Hoon;
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 Abstract
Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.
 Keywords
Bayes risk;bootstrap;cross-validation;
 Language
Korean
 Cited by
 References
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