Contributions to Supervised Learning of Real-Valued Functions Using Neural Networks
This dissertation presents a new strategy for the automatic design of neural networks. The learning environment addressed is supervised learning from examples. Specifically, Radial Basis Functions (RBF) networks learning real-valued functions of real vectors as in non-linear regression applications...
| Main Author: | |
|---|---|
| Published: |
NSUWorks
1998
|
| Subjects: | |
| Online Access: | http://nsuworks.nova.edu/gscis_etd/395 |