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Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps
Author
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-24, 24 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-17
Country of Publication
Egypt
No. of Pages
24
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Kamimura, Ryotaro. 2014. Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-24.
https://search.emarefa.net/detail/BIM-1049461
Modern Language Association (MLA)
Kamimura, Ryotaro. Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps. The Scientific World Journal No. 2014 (2014), pp.1-24.
https://search.emarefa.net/detail/BIM-1049461
American Medical Association (AMA)
Kamimura, Ryotaro. Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-24.
https://search.emarefa.net/detail/BIM-1049461
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049461