On $p$-adic Classification

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Jun 24, 2009
Patrick Erik Bradley
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A $p$-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 $p$ with finitely many exceptions. The methods are applied to the construction of $p$-adic classifiers in the context of learning.

* p-Adic Numbers, Ultrametric Analysis, and Applications, Vol. 1, No. 4 (2009), 271-285  * 16 pages, 7 figures, 1 table; added reference, corrected typos, minor content changes  
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