Abstract
In this paper, we propose a self-organizing map association (SOMA) network which associates a perfect information from a part of the information. The processes of the SOMA network are divided into a learning mode and an association mode. In the learning mode, the similar perfect informations are represented by a few units on the competitive layer. In the association mode, when the information, whose parts are lost, is applied to the SOMA network, the lost part is associated. The performance of the SOMA network is evaluated by applying to the association of the data used in the orthodontics.
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© 2001 Springer-Verlag London Limited
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Yamakawa, T., Horio, K., Kubota, R. (2001). A SOM Association Network. In: Advances in Self-Organising Maps. Springer, London. https://doi.org/10.1007/978-1-4471-0715-6_3
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DOI: https://doi.org/10.1007/978-1-4471-0715-6_3
Publisher Name: Springer, London
Print ISBN: 978-1-85233-511-3
Online ISBN: 978-1-4471-0715-6
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