Avoiding the accuracy-simplicity trade-off in pattern recognition

H. John Caulfield

doi:10.1117/12.512617

30 December 2003 Avoiding the accuracy-simplicity trade-off in pattern recognition

H. John Caulfield

Proceedings Volume 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI; (2003) https://doi.org/10.1117/12.512617
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

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.

Citation Download Citation

H. John Caulfield "Avoiding the accuracy-simplicity trade-off in pattern recognition", Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); https://doi.org/10.1117/12.512617

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