Name
Abstract | ||
|---|---|---|
This paper proposes the multicolumn RBF network (MCRN) as a method to improve the accuracy and speed of a traditional radial basis function network (RBFN). The RBFN, as a fully connected artificial neural network (ANN), suffers from costly kernel inner-product calculations due to the use of many instances as the centers of hidden units. This issue is not critical for small datasets, as adding more... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2650865 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Training,Kernel,Testing,Machine learning,Radial basis function networks,Convergence | Journal | 29 |
Issue | ISSN | Citations |
4 | 2162-237X | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ammar O. Hoori | 1 | 1 | 0.68 |
| Yuichi Motai | 2 | 230 | 24.68 |