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Proceedings Paper

Handling small training sets confidence/accuracy with regard to new examples
Author(s): H. John Caulfield
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Paper Abstract

It often happens that the number of samples available to train a discriminator is many fewer than Learning Theory tells us we need to accomplish the required accuracy/confidence. When you run up against a theoretical limit, only two choices are possible. You can accept the situation, or you can look for ways around those limits. This report suggests that there is a way around conventional learning theory and applies the new technique (called Margin Setting) to a difficult artificial problem to illustrate its power.

Paper Details

Date Published: 6 August 2003
PDF: 6 pages
Proc. SPIE 5106, Optical Pattern Recognition XIV, (6 August 2003); doi: 10.1117/12.484828
Show Author Affiliations
H. John Caulfield, Alabama A&M Univ. Research Institute (United States)
Fisk University (United States)

Published in SPIE Proceedings Vol. 5106:
Optical Pattern Recognition XIV
David P. Casasent; Tien-Hsin Chao, Editor(s)

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