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2013 Cellular tree classifiers
Gérard Biau, Luc Devroye
Electron. J. Statist. 7: 1875-1912 (2013). DOI: 10.1214/13-EJS829


The cellular tree classifier model addresses a fundamental problem in the design of classifiers for a parallel or distributed computing world: Given a data set, is it sufficient to apply a majority rule for classification, or shall one split the data into two or more parts and send each part to a potentially different computer (or cell) for further processing? At first sight, it seems impossible to define with this paradigm a consistent classifier as no cell knows the “original data size”, $n$. However, we show that this is not so by exhibiting two different consistent classifiers. The consistency is universal but is only shown for distributions with nonatomic marginals.


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Gérard Biau. Luc Devroye. "Cellular tree classifiers." Electron. J. Statist. 7 1875 - 1912, 2013.


Published: 2013
First available in Project Euclid: 10 July 2013

zbMATH: 1293.62067
MathSciNet: MR3084675
Digital Object Identifier: 10.1214/13-EJS829

Primary: 62G05
Secondary: 62G20

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society


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