DocumentCode :
3596589
Title :
Self-organizing concept maps
Author :
Hagiwara, Masafumi
Author_Institution :
Dept. of Electr. Eng., Keio Univ., Yokohama, Japan
Volume :
1
fYear :
1995
Firstpage :
447
Abstract :
Self-organizing concept maps (SOCOMs) based on a neural network model are proposed in this paper. They can arrange concepts or words in a map space using Kohonen´s self-organizing map algorithm. One of the most important advantages of the proposed maps is that they employ the idea of k-nearest neighbor (k-NN): they do not require all of the data among concepts or words. The author proposes two kinds of SOCOMs: one is a metric SOCOM, another is a non-metric one. The metric SOCOM uses the information about the metric data such as similarity. The non-metric one uses the information about the rank order of similarity among items. The combination of the idea of k-NN and a non-metric SOCOM is effective to relax the severe requirements on data: it does not require all of the detailed metric information among concepts or words. Computer simulation results have shown the effectiveness of the proposed SOCOM
Keywords :
self-organising feature maps; Kohonen´s self-organizing map algorithm; k-nearest neighbor; neural network model; rank order of similarity; self-organizing concept maps; Artificial neural networks; Backpropagation algorithms; Clustering algorithms; Computer simulation; Feature extraction; Image processing; Neurons; Pattern recognition; Space technology; Supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Print_ISBN :
0-7803-2559-1
Type :
conf
DOI :
10.1109/ICSMC.1995.537800
Filename :
537800
Link To Document :
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