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Cellular Tree Classifiers

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.
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Contributor : Gérard Biau <>
Submitted on : Monday, June 24, 2013 - 9:19:37 PM
Last modification on : Tuesday, August 4, 2020 - 3:49:16 AM
Document(s) archivé(s) le : Wednesday, September 25, 2013 - 4:12:11 AM


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  • HAL Id : hal-00778520, version 2
  • ARXIV : 1301.4679


Gérard Biau, Luc Devroye. Cellular Tree Classifiers. 2013. ⟨hal-00778520v2⟩



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