DocumentCode :
315249
Title :
An improved expand-and-truncate learning
Author :
Yamamoto, Atsushi ; Saito, Toshimichi
Author_Institution :
Dept. of Electron. & Electr. Eng., Hosei Univ., Tokyo, Japan
Volume :
2
fYear :
1997
fDate :
9-12 Jun 1997
Firstpage :
1111
Abstract :
This paper proposes a novel learning algorithm that can realize any binary-to-binary mapping by using three-layer binary neural networks. The algorithm includes an improved expand-and-truncate learning routine that can reduce the number of the hidden neurons by conventional methods. Also, the output layer parameters can be given by simple analytic formulae
Keywords :
learning (artificial intelligence); multilayer perceptrons; binary-to-binary mapping; expand-and-truncate learning; hidden neurons; three-layer binary neural networks; Equations; Gravity; Hypercubes; Neural networks; Neurons;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
Type :
conf
DOI :
10.1109/ICNN.1997.616185
Filename :
616185
Link To Document :
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