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The Scientific World Journal - Volume 2014 2014, Article ID 397927, 24 pages -

Research ArticleIT Education Center and School of Science and Technology, 1117 Kitakaname, Hiratsuka, Kanagawa 259-1292, Japan

Received 29 April 2014; Accepted 15 August 2014; Published 18 September 2014

Academic Editor: Juan M. Corchado

Copyright © 2014 Ryotaro Kamimura. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called -cooperation-controlled learning.- In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps.

Autor: Ryotaro Kamimura



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