Novel geometrical supervised-learning scheme

Chia-Lun John Hu

doi:10.1117/12.21194

1 August 1990 Novel geometrical supervised-learning scheme

Chia-Lun John Hu

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21194
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

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

Citation Download Citation

Chia-Lun John Hu "Novel geometrical supervised-learning scheme", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21194

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available