DocumentCode
327648
Title
Kohonen networks and the influence of training on data structures
Author
Morlini, Isabella
fYear
1998
fDate
31 Aug-2 Sep 1998
Firstpage
370
Lastpage
379
Abstract
The Kohonen feature map is applied to the so-called two-spiral problem. Even if this network is unsupervised, the results indicate that the ability to classify or visualize the data structure depends on the training parameters. The example shows, therefore, that the network self-organization can be limited and the choices of the researcher can strongly affect the network output
Keywords
data structures; learning (artificial intelligence); pattern classification; self-organising feature maps; Kohonen feature map; Kohonen networks; data structures; network self-organization; training; two-spiral problem; Algorithm design and analysis; Clustering algorithms; Convergence; Data structures; Data visualization; Euclidean distance; Multidimensional systems; Neural networks; Partitioning algorithms; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
Conference_Location
Cambridge
ISSN
1089-3555
Print_ISBN
0-7803-5060-X
Type
conf
DOI
10.1109/NNSP.1998.710667
Filename
710667
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