Title :
A hybrid model to infer US-Japan trade relations
Author :
Kamimura, Ryotam ; Yoshida, Fumihiko
Author_Institution :
Inf. Sci. Lab., Tokai Univ., Kanagawa, Japan
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;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1198984
