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Proceedings Paper

Neighborhoods and trajectories in Kohonen maps
Author(s): Alexander Grunewald
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Paper Abstract

The Kohonen map is a basic paradigm of unsupervised learning. Quite a few descriptions exist of the possibility to expand other paradigms, and to describe their output behavior, for example, the functions that can be learned and the trajectories in the output space. The main parameters of Kohonen maps are the underlying topology and the metric used. In this paper the concepts of nearest neighbor, neighbor, neighborhood and underlying topology are formalized in a set-theoretic manner and thus expanded. Similarly, the concept of metric is enhanced by the introduction of similarity measures. A theorem on continuity of output is proved for such measures.

Paper Details

Date Published: 1 July 1992
PDF: 10 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140152
Show Author Affiliations
Alexander Grunewald, Boston Univ. (United States)


Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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