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.
| Original language | English (US) |
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| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 1658-1663 |
| Number of pages | 6 |
| Volume | 3 |
| State | Published - 1996 |
| Event | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA Duration: Jun 3 1996 → Jun 6 1996 |
Other
| Other | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) |
|---|---|
| City | Washington, DC, USA |
| Period | 6/3/96 → 6/6/96 |
ASJC Scopus subject areas
- Software