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Abstract: Besides their topological properties, Kohonen maps are often used for vector quantization only. These auto-organised networks are often compared to other standard and/or adaptive vector quantization methods, and, according to the large literature on the subject, show either better or worst properties in terms of quantization, speed of convergence, approximation of probability densities, clustering,... The purpose of this paper is to define more precisely some commonly encountered problems, and... (Update)
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...de Tolbiac, F 75634 Pads Cedex 13, France 3 Universitd Catholique de Louvain, DICE, 3 pl. du Levant, Abstract. In a previous paper ([1], ESANN 97) we compared the Kohonen algorithm (SAM) to Simple Competitive Learning Algorithm (SCL) when the goal is to reconstruct an...
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BibTeX entry: (Update)
Cottrell M., de Bodt E., Verleysen M., Kohonen maps versus vector quantization for data analysis, in Proc of ESANN, M. Verleysen ED., D Facto, Brussels, 1997. http://citeseer.ist.psu.edu/debodt97kohonen.html More
@incollection{ debodt97kohonen,
author = "Eric {de Bodt} and Michel Verleysen and Marie Cottrell",
title = "{K}ohonen maps versus vector quantization for data analysis",
booktitle = "Proc. {ESANN}'97, 5th European Symposium on Artificial Neural Networks",
publisher = "D facto",
address = "Brussels, Belgium",
editor = "Michel Verleysen",
pages = "211--218",
year = "1997",
url = "citeseer.ist.psu.edu/debodt97kohonen.html" }
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