DocumentCode :
395136
Title :
Multi-branch structure of layered neural networks
Author :
Yamashita, Takashi ; Hirasawa, Kotaro ; Hu, Jinglu ; Murata, Jnichi
Author_Institution :
Graduate Sch. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan
Volume :
1
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
243
Abstract :
In this paper, a multi-branch structure of neural networks is studied to make their size compact. The multi-branch structure has shown improved performance against conventional neural networks. As a result, it has been proved that the number of nodes of networks and the computational cost for training networks can be reduced. In addition, it could be said that proposed multi-branch networks are special cases of higher order neural networks, however, they obtain higher order effect easier without suffering the parameter explosion problem.
Keywords :
neural nets; computational cost; higher order neural networks; layered neural networks; multibranch structure; network training; Backpropagation algorithms; Computational efficiency; Computer networks; Costs; Explosions; Gradient methods; Information science; Neural networks; Production systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1202170
Filename :
1202170
Link To Document :
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