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Proceedings Paper

Extreme patterns in noniterative learning studied from N-dimension geometry
Author(s): Chia-Lun John Hu
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Paper Abstract

Neural networks with discrete neurons cannot be studied form the differential-integral formulation because of the mathematical ill-behaviors in continuous differentiation. But they can be studied quite conveniently with discrete mathematics and N-D geometry. This paper derives the learning system of the discrete neural networks and then solve the learning problem with a mathematically very efficient scheme - the noniterative scheme using the concept of N-D geometry. The solution involves a novel approach of finding the extreme edges of a set of training pattern vectors which form a convex cone in the N-D space.

Paper Details

Date Published: 6 March 2002
PDF: 5 pages
Proc. SPIE 4734, Optical Pattern Recognition XIII, (6 March 2002); doi: 10.1117/12.458414
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)

Published in SPIE Proceedings Vol. 4734:
Optical Pattern Recognition XIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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