Additive Composition of Supervised Self-Organizing Maps |
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Authors: | Buessler Jean-Luc Urban Jean-Philippe Gresser Julien |
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Affiliation: | (1) TROP Research Group, 4 rue des Frères Lumière, F-68093 Mulhouse Cedex, France |
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Abstract: | The learning of complex relationships can be decomposed into several neural networks. The modular organization is determined
by prior knowledge of the problem that permits to split the processing into tasks of small dimensionality. The sub-tasks can
be implemented with neural networks, although the learning examples cannot be used anymore to supervise directly each of the
networks. This article addresses the problem of learning in a modular context, developing in particular additive compositions. A simple rule allows defining efficient training, and combining, for example, several Supervised-SOM networks. This technique
is important because it introduces interesting generalizations in many modular compositions, permitting data fusion or sequential
combinations of neural networks.
This revised version was published online in August 2006 with corrections to the Cover Date. |
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Keywords: | additive regression modular architecture on-line learning self-organizing maps supervised learning |
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