6 August 2003 Handling small training sets confidence/accuracy with regard to new examples
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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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
H. John Caulfield, "Handling small training sets confidence/accuracy with regard to new examples", Proc. SPIE 5106, Optical Pattern Recognition XIV, (6 August 2003); doi: 10.1117/12.484828; https://doi.org/10.1117/12.484828
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