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Pré-Publication, Document De Travail Année : 2013

Cellular Tree Classifiers

Résumé

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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Dates et versions

hal-00778520 , version 1 (20-01-2013)
hal-00778520 , version 2 (24-06-2013)

Identifiants

Citer

Gérard Biau, Luc Devroye. Cellular Tree Classifiers. 2013. ⟨hal-00778520v1⟩
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