ISSN:
1573-773X
Keywords:
self-organizing feature map
;
learning
;
parameter
;
lateral connection radius
;
competition
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract The behavior of self-organizing feature maps is critically dependent on parameters such as lateral connection radius, lateral inhibition intensity, and network size. With no theoretical guidelines for the choice of these parameters, they are usually selected through a trial-and-error process. In order to provide heuristic guidelines for future model designers as well as to give insight into which model features are responsible for specific aspects of maps, we systematically varied these parameters and studied their effects on the properties of a self-organizing feature map. The connectivity radius was found to determine the size of activation clusters quadratically. As the intensity of lateral inhibition was varied, feature patterns varied from stripe-like to clusters in the map, with other intermediate patterns also occurring. The number of clusters of each feature increased nonlinearly as the network size increased.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00454846
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