DocumentCode
288344
Title
Delta training strategies
Author
Cloete, Ian ; Ludik, Jacques
Author_Institution
Dept. of Comput. Sci., Stellenbosch Univ., South Africa
Volume
1
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
295
Abstract
The training strategy used in connectionist learning has been overlooked as a method to improve learning time. We suggest several new strategies for backpropagation learning for both feedforward and recurrent architectures and show experimentally that they produce much faster convergence compared to conventional training using a fixed set. In particular, the Delta training strategy produced the best improvement of 76%
Keywords
backpropagation; feedforward neural nets; recurrent neural nets; Delta training strategies; backpropagation learning; connectionist learning; feedforward architectures; fixed set; learning time; recurrent architectures; Africa; Artificial neural networks; Computer architecture; Computer science; Convergence; Euclidean distance; Merging; Neural networks; Sun;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374178
Filename
374178
Link To Document