On $p$-adic Classification
Machine Learning
2009-12-01 v2
Abstract
A -adic modification of the split-LBG classification method is presented in which first clusterings and then cluster centers are computed which locally minimise an energy function. The outcome for a fixed dataset is independent of the prime number with finitely many exceptions. The methods are applied to the construction of -adic classifiers in the context of learning.
Keywords
Cite
@article{arxiv.0903.2870,
title = {On $p$-adic Classification},
author = {Patrick Erik Bradley},
journal= {arXiv preprint arXiv:0903.2870},
year = {2009}
}
Comments
16 pages, 7 figures, 1 table; added reference, corrected typos, minor content changes