Name
Abstract | ||
|---|---|---|
A multi-layer self-organizing neural network model has been proposed for computation of the convex-layers of a given set of planar points. Computation of convex-layers has been found to be useful in pattern recognition and in statistics. The proposed network architecture evolves in such a manner that it adapts itself to the hull-vertices of the convex-layers in the required order. Time complexity of the proposed model is also discussed. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30499-9_99 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
self organization,time complexity,neural network,neural network model,network architecture,pattern recognition | Computer science,Computational geometry,Self-organization,Network architecture,Algorithm,Convex hull,Time delay neural network,Artificial intelligence,Artificial neural network,Time complexity,Machine learning,Computation | Conference |
Volume | ISSN | Citations |
3316 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amitava Datta | 1 | 734 | 81.63 |
| Srimanta Pal | 2 | 242 | 32.13 |