Extreme patterns in noniterative learning studied from N-dimension geometry

Chia-Lun John Hu

doi:10.1117/12.458414

6 March 2002 Extreme patterns in noniterative learning studied from N-dimension geometry

Chia-Lun John Hu

Proceedings Volume 4734, Optical Pattern Recognition XIII; (2002) https://doi.org/10.1117/12.458414
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Chia-Lun John Hu "Extreme patterns in noniterative learning studied from N-dimension geometry", Proc. SPIE 4734, Optical Pattern Recognition XIII, (6 March 2002); https://doi.org/10.1117/12.458414

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