DocumentCode
303416
Title
Hierarchical growing cell structures
Author
Burzevski, Vanco ; Mohan, Chilukuri K.
Author_Institution
Sch. of Comput. & Inf. Sci., Syracuse Univ., NY, USA
Volume
3
fYear
1996
fDate
3-6 Jun 1996
Firstpage
1658
Abstract
We propose a hierarchical self-organizing neural network with adaptive architecture and simple topological organization. This network combines features of Fritzke´s growing cell structures and traditional hierarchical clustering algorithms. The height and width of the tree structure depend on the user-specified level of error desired, and the weights in upper layers of the network do not change in later phases of the learning algorithm
Keywords
adaptive systems; learning (artificial intelligence); network topology; neural net architecture; self-organising feature maps; trees (mathematics); adaptive architecture; hierarchical clustering; hierarchical growing cell structures; learning algorithm; self-organizing neural network; topological organization; tree structure; Adaptive systems; Clustering algorithms; Computational Intelligence Society; Frequency; Learning systems; Network topology; Neural networks; Plastics; Stability; Tree data structures;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.549149
Filename
549149
Link To Document