DocumentCode
2168161
Title
Stochastic backpropagation: a learning algorithm for generalization problems
Author
Ramamoorthy, C.V. ; Shekhar, Shashi
Author_Institution
Div. of Comput. Sci., California Univ., Berkeley, CA, USA
fYear
1989
fDate
20-22 Sep 1989
Firstpage
664
Lastpage
671
Abstract
Neural networks have traditionally been applied to recognition problems, and most learning algorithms are tailored to those problems. The authors discuss the requirements of learning for generalization, which is NP-complete and cannot be approached by traditional methods based on gradient descent. They present a stochastic learning algorithm based on simulated annealing in weight space. The convergence properties and feasibility of the algorithm are verified
Keywords
learning systems; neural nets; NP-complete; convergence properties; generalization problems; learning algorithm; neural networks; simulated annealing; stochastic backpropagation; Backpropagation algorithms; Computer science; Convergence; Neural networks; Noise shaping; Shape; Simulated annealing; Speech recognition; Stochastic processes; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Software and Applications Conference, 1989. COMPSAC 89., Proceedings of the 13th Annual International
Conference_Location
Orlando, FL
Print_ISBN
0-8186-1964-3
Type
conf
DOI
10.1109/CMPSAC.1989.65163
Filename
65163
Link To Document