DocumentCode :
1809652
Title :
Visualization of radial basis function networks
Author :
Agogino, Adrian ; Ghosh, Joydeep ; Martin, Cheryl
Author_Institution :
Lab. for Artificial Neural Syst., Texas Univ., Austin, TX, USA
Volume :
2
fYear :
1999
fDate :
36342
Firstpage :
1199
Abstract :
Presents a method for the 3D visualization of the structure of radial basis function networks. This method allows the visualization of basis function characteristics (centers and widths) along with second level weights. Network properties can be displayed simultaneously with the training data or test data in the same input space. Principal component analysis is used to transform the input data so that its most salient dimensions can be visualized. This method also allows changes made while graphically editing the network structure, in transformed space, to be projected back into the original input space
Keywords :
data visualisation; neural net architecture; pattern recognition; principal component analysis; radial basis function networks; 3D visualization; basis function characteristics; network properties; second level weights; Artificial intelligence; Bonding; Data visualization; Intelligent structures; Intelligent systems; Laboratories; Network topology; Neural networks; Radial basis function networks; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.831130
Filename :
831130
Link To Document :
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