Title :
Kohonen networks and the influence of training on data structures
Author :
Morlini, Isabella
fDate :
31 Aug-2 Sep 1998
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;
Conference_Titel :
Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
Conference_Location :
Cambridge
Print_ISBN :
0-7803-5060-X
DOI :
10.1109/NNSP.1998.710667
