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Speculate-correct error bounds for k -nearest neighbor classifiers (2019)

Springer

In: Machine Learning

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Publication Date: 2019

Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that 〈em〉k〈/em〉-nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with 〈span〉 〈span〉\(\hbox {O} \left( \sqrt{(k + \ln n)/n} \right) \)〈/span〉 〈/span〉 range around an estimate of generalization error for 〈em〉n〈/em〉 in-sample examples. 〈/p〉

Print ISSN: 0885-6125

Electronic ISSN: 1573-0565

Topics: Computer Science

Published by Springer

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