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

Abstract

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.

Citation

Download Citation

Gérard Biau. Luc Devroye. "Cellular tree classifiers." Electron. J. Statist. 7 1875 - 1912, 2013. https://doi.org/10.1214/13-EJS829

Information

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

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

Subjects:
Primary: 62G05
Secondary: 62G20

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

JOURNAL ARTICLE
38 PAGES


SHARE
Back to Top