DocumentCode :
3269736
Title :
Primacy and recency effects due to momentum in back-propagation
Author :
Goggin, S.D.D. ; Johnson, K.M. ; Gustafson, K.
Author_Institution :
Center for Optoelectron. Comput. Sci., Colorado Univ., Boulder, CO, USA
fYear :
1989
fDate :
0-0 1989
Abstract :
Summary form only given, as follows. Primacy and recency effects are analyzed mathematically for backpropagation algorithms (generalized delta rule), which use momentum. The results show that when the conventional momentum parameter is used, a primary effect occurs: the current values of the weights are biased toward the first presentations in a sequence of training patterns. To produce a recency effect, a different momentum parameter is introduced. The current values of the weights depend more on recent presentations of training patterns under this recency effect. A method is provided for selecting a momentum parameter based on the effect desired: primacy or recency.<>
Keywords :
learning systems; neural nets; artificial intelligence; backpropagation; delta rule; learning systems; momentum parameter; neural nets; primacy effects; recency effects; training patterns; Learning systems; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
Type :
conf
DOI :
10.1109/IJCNN.1989.118516
Filename :
118516
Link To Document :
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