# Cellular Tree Classifiers

4 CLASSIC - Computational Learning, Aggregation, Supervised Statistical, Inference, and Classification
DMA - Département de Mathématiques et Applications - ENS Paris, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt
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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Cited literature [49 references]

https://hal.archives-ouvertes.fr/hal-00778520
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

### Files

articlegreedy6.pdf
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### Identifiers

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

### Citation

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

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