DocumentCode
394409
Title
A hybrid model to infer US-Japan trade relations
Author
Kamimura, Ryotam ; Yoshida, Fumihiko
Author_Institution
Inf. Sci. Lab., Tokai Univ., Kanagawa, Japan
Volume
4
fYear
2002
fDate
18-22 Nov. 2002
Firstpage
1800
Abstract
In this paper, we propose a new neural model in which the information maximization and error minimization components are combined. Since information is maximized, information is compressed into networks in explicit ways, which enables us to discover the salient features in input patterns. We applied this method to a problem of US-Japan trade relations. Experimental results confirmed that, due to the maximized information in competitive units, easily interpretable internal representations can be obtained.
Keywords
feature extraction; international trade; learning (artificial intelligence); neural nets; Japan; USA; error minimization; hybrid model; information maximization; learning; neural model; neural networks; salient feature extraction; trade relations; Convergence; Data mining; Feature extraction; Inference mechanisms; Information science; Learning systems; Minimization methods; Neural networks; Uncertainty;
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.2002.1198984
Filename
1198984
Link To Document