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Proceedings Paper

Avoiding the accuracy-simplicity trade-off in pattern recognition
Author(s): H. John Caulfield
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Paper Abstract

Statistical pattern recognition begins with a training set of what we hope are fair samples from multiple sets and seeks to devise a rule whereby new samples (not in the training set) are likely to be classified accurately. In so doing it seeks simple classifiers not likely to be attending either to noise or the extraneous in the training set examples, but it also seeks accuracy in classifying members of the training set. It is provable that the optimum lies in a compromise between accuracy and simplicity. I show here a way to achieve both good things at once and hence free pattern recognition of this crippling central tradeoff.

Paper Details

Date Published: 30 December 2003
PDF: 6 pages
Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); doi: 10.1117/12.512617
Show Author Affiliations
H. John Caulfield, Alabama A&M Univ. (United States)
Fisk Univ. (United States)


Published in SPIE Proceedings Vol. 5200:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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