DocumentCode
1131954
Title
Generalization by neural networks
Author
Shekhar, Shashi ; Amin, Minesh B.
Author_Institution
Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN, USA
Volume
4
Issue
2
fYear
1992
fDate
4/1/1992 12:00:00 AM
Firstpage
177
Lastpage
185
Abstract
The authors discuss the requirements of learning for generalization, where the traditional methods based on gradient descent have limited success. A stochastic learning algorithm based on simulated annealing in weight space is presented. The authors verify the convergence properties and feasibility of the algorithm. An implementation of the algorithm and validation experiments are described
Keywords
learning systems; neural nets; simulated annealing; convergence properties; generalization; gradient descent; learning; neural networks; simulated annealing; stochastic learning algorithm; weight space; Algorithm design and analysis; Annealing; Backpropagation algorithms; Convergence; Curve fitting; Handwriting recognition; Neural networks; Noise shaping; Stochastic processes; Testing;
fLanguage
English
Journal_Title
Knowledge and Data Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1041-4347
Type
jour
DOI
10.1109/69.134256
Filename
134256
Link To Document