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On p-adic classification

On p-adic classification A p-adic modification of the split-LBG classification method is presented in which first clusterings and then cluster centers are computed which locally minimize an energy function. The outcome for a fixed dataset is independent of the prime number p with finitely many exceptions. The methods are applied to the construction of p-adic classifiers in the context of learning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png P-Adic Numbers, Ultrametric Analysis, and Applications Springer Journals

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References (10)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Pleiades Publishing, Ltd.
Subject
Mathematics; Algebra
ISSN
2070-0466
eISSN
2070-0474
DOI
10.1134/S2070046609040013
Publisher site
See Article on Publisher Site

Abstract

A p-adic modification of the split-LBG classification method is presented in which first clusterings and then cluster centers are computed which locally minimize an energy function. The outcome for a fixed dataset is independent of the prime number p with finitely many exceptions. The methods are applied to the construction of p-adic classifiers in the context of learning.

Journal

P-Adic Numbers, Ultrametric Analysis, and ApplicationsSpringer Journals

Published: Nov 15, 2009

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