DocumentCode
1242501
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
Volume
6
Issue
2
fYear
1995
fDate
3/1/1995 12:00:00 AM
Firstpage
512
Lastpage
515
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;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.363492
Filename
363492
Link To Document