Among the architectures and algorithms suggested for artificial neural networks, the Self-Organizing Map has the special property of effectively creating spatially organized "internal representations" of various features of input signals and their abstractions.
One novel result is that the self-organization process can also discover semantic relationships in sentences. In this respect the resulting maps very closely resemble the topographically organized maps found in the cortices of the more developed animal brains. After supervised fine tuning of its weight vectors, the Self Organizing Map has been particularly successful in various pattern recognition tasks involving very noisy signals. In particular, these maps have been used in practical speech recognition, and work is in progress on their application to robotics, process control, tele communications, etc. this paper contains a survey of several basic facts and results.
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KOHONEN, T., 1990. The self-organizing map. Proceedings of the IEEE. September 1990. Vol. 78, no. 9, p. 1464–1480. DOI 10.1109/5.58325.
The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain Maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed.<>
Introduction: What is a Self-Organizing Map? pdf 