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Proceedings Paper

Novel geometrical supervised-learning scheme
Author(s): Chia-Lun John Hu
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Paper Abstract

This paper describes a novel learning scheme derived from a geometrical study of a onelayer autoassociative neural net that possesses hard limited neuron response functions. It is a study of the nonlinear mapping relations based on the concept of convex cone in the Ndimensional state space. (N is the nurrber of neurons in the neural system. ) This theoretical approach then allows us to derive a new learning scheme that appears to have many advantages over the conventional systems. 1 . It is a very fast and efficient onestep learning scheme . It does not require iteration processes to achieve the learning. 2 . Learning new mappings will not destroy old mappings already learned. 3. Learning of DISCRETE (or binery) mapping relations allows us to do pattern recognition in CONTINUOUS (analog) manner. 4. The maximum capacity of learning is much larger than those of the conventional systems. 5. It should be very easy to implement with conventional electronic coriponents. I . IrRWlXTI Supervised learning of a neural net has been studied quited extensively in the past decade. Besides the classical outerproduct rule [1] and the Ilebb'' 5 rule [2] many other supervised learning rules have also been studied and applied in practical areas. Rumelhart Hinton and Williams [3] derived the delta learning rule based on the gradient descent iteration approach. Widrow Hoff al et [46] derived another learning rule that forces

Paper Details

Date Published: 1 August 1990
PDF: 7 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21194
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ. (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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