Title :
Learning in multilayered networks used as autoassociators
Author :
Bianchini, M. ; Frasconi, P. ; Gori, M.
Author_Institution :
Dipartimento de Sistemi e Inf., Firenze Univ., Italy
fDate :
3/1/1995 12:00:00 AM
Abstract :
Gradient descent learning algorithms may get stuck in local minima, thus making the learning suboptimal. In this paper, we focus attention on multilayered networks used as autoassociators and show some relationships with classical linear autoassociators. In addition, by using the theoretical framework of our previous research, we derive a condition which is met at the end of the learning process and show that this condition has a very intriguing geometrical meaning in the pattern space
Keywords :
content-addressable storage; learning (artificial intelligence); multilayer perceptrons; autoassociators; geometrical meaning; gradient descent learning algorithms; multilayered networks; pattern space; Backpropagation; Convergence; Costs; Intelligent networks; Linearity; Neurons; Pattern analysis; Rough surfaces; Shape; Surface roughness;
Journal_Title :
Neural Networks, IEEE Transactions on
