DocumentCode :
3320342
Title :
When is the generalized delta rule a learning rule? a physical analogy
Author :
Pemberton, Joseph C. ; Vidal, Jacques J.
Author_Institution :
Dept. of Comput. Sci., California Univ., Los Angeles, CA, USA
fYear :
1988
fDate :
24-27 July 1988
Firstpage :
309
Abstract :
The authors show that under some conditions the weights and threshold obtained under the linear generalized delta rule can be calculated a priori. The analysis is illustrated by a physical analogy. The steady-state weight vector produced by the generalized delta rule can be equated to the center of mass of a collection of particles placed at corners of a hypercube defined by the weights and threshold. The result is a direct mapping from the input and target signals onto the weight-threshold hypercube.<>
Keywords :
artificial intelligence; learning systems; artificial intelligence; delta rule; direct mapping; learning rule; weight vector; weight-threshold hypercube; Artificial intelligence; Learning systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1988., IEEE International Conference on
Conference_Location :
San Diego, CA, USA
Type :
conf
DOI :
10.1109/ICNN.1988.23862
Filename :
23862
Link To Document :
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