Name
Abstract | ||
|---|---|---|
Using radial basis function networks for function approxima- tion tasks suffers from unavailable knowledge about an adequate network size. In this work, a measuring technique is proposed which can control the model complexity and is based on the correlation coefficient between two basis functions. Simulation results show good performance and, therefore, this technique can be integrated in the RBF training procedure. |
Year | Venue | Keywords |
|---|---|---|
2007 | ESANN | radial basis function network |
Field | DocType | Citations |
Network size,Data mining,Correlation coefficient,Radial basis function network,Radial basis function,Pattern recognition,Computer science,Hierarchical RBF,Artificial intelligence,Basis function,Machine learning,Model complexity | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| U. Rückert | 1 | 755 | 103.61 |
| Ralf Eickhoff | 2 | 65 | 12.37 |