DocumentCode
288403
Title
Self-organized learning of 3 dimensions
Author
Szepesvári, Cs ; Lörincz, A.
Author_Institution
Inst. of Isotopes, Hungarian Acad. of Sci., Budapest, Hungary
Volume
2
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
671
Abstract
The geometric learning capabilities of a competitive neural network are studied. It is shown that the appropriate selection of a neural activity function enables the learning of the 3D geometry of a world, from two of the 2D projections of 3D extended objects
Keywords
Hebbian learning; self-organising feature maps; solid modelling; spatial reasoning; 2D projections; 3D extended objects; 3D geometry; competitive neural network; geometric learning capabilities; neural activity function selection; self-organized learning; Artificial neural networks; Geometry; Isotopes; Neural networks; Neurofeedback; Neurons; Organizing; Software algorithms; Spatial filters; Wire;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374256
Filename
374256
Link To Document