Handling small training sets confidence/accuracy with regard to new examples

H. John Caulfield

doi:10.1117/12.484828

6 August 2003 Handling small training sets confidence/accuracy with regard to new examples

H. John Caulfield

Proceedings Volume 5106, Optical Pattern Recognition XIV; (2003) https://doi.org/10.1117/12.484828
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

H. John Caulfield "Handling small training sets confidence/accuracy with regard to new examples", Proc. SPIE 5106, Optical Pattern Recognition XIV, (6 August 2003); https://doi.org/10.1117/12.484828

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