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Cellular tree classifiers. (English) Zbl 1293.62067
Summary: 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.

##### MSC:
 62G05 Nonparametric estimation 62G20 Asymptotic properties of nonparametric inference 62H30 Classification and discrimination; cluster analysis (statistical aspects) 68T10 Pattern recognition, speech recognition
C4.5
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