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 :
بازگشت