Abstract
The Multi-SOM–Meta-SOM system is a supervised ANN model able to create future maps of the input. Therefore, it not only is able to correctly classify the input basing on an external target but can also provide information concerning the articulation of classes and their relationships with each other. The Multi-SOM–Meta-SOM system is composed of two different nets: the first one (Multi-SOM) is supervised, while the second one (Meta-SOM), which is not supervised, processes the weights of the first one and reproduces a classificatory output. The Multi-SOMs are influenced by the SOM networks, but they upset their structure, transforming them into supervised networks; the Meta-SOM networks maintain the structure and purposes of the SOMs, but their input is constituted by the models of the input classes created by the Multi-SOMs.
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Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59–69.
Anderson, J. A. and Rosenfeld, E. (Eds.) (1988). Neurocomputing foundations of research. Cambridge, MA, The MIT Press.
Kohonen, T. (1984). Self-Organization and associative memories. Vol. 8: Springer series in information sciences. Berlin: Springer-Verlag.
Kohonen, T. (1988). Learning vector quantization, Neural Networks 1, 303.
Kohonen, T. (1990). The Self-organizing map. Proceedings IEEE, 78, 1464–1480.
Kohonen, T. (1995). Self-organizing maps. Berlin, Heidelberg: Springer-Verlag.
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Massini, G. (2010). Multi–Meta-SOM. In: Capecchi, V., Buscema, M., Contucci, P., D'Amore, B. (eds) Applications of Mathematics in Models, Artificial Neural Networks and Arts. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8581-8_13
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DOI: https://doi.org/10.1007/978-90-481-8581-8_13
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