Rüger, Stefan
(1998).
A class of asymptotically stable algorithms for learning-rate adaptation.
Algorithmica, 22(1-2) pp. 198–210.
Abstract
A stability criterion for learning is given. In the case of learning-rate adaptation of backpropagation, a class of asymptotically stable algorithms is presented and studied, including a convergence proof. Simulations demonstrate relevance and limitations.
| Item Type: |
Journal Article
|
| Copyright Holders: |
1998 Springer-Verlag |
| ISSN: |
1432-0541 |
| Keywords: |
convex optimization; neural learning; learning-rate adaptation; line search; stability criterion |
| Academic Unit/Department: |
Knowledge Media Institute |
| Item ID: |
11955 |
| Depositing User: |
Users 8580 not found. |
| Date Deposited: |
08 Oct 2008 13:13 |
| Last Modified: |
19 Mar 2014 15:33 |
| URI: |
http://oro.open.ac.uk/id/eprint/11955 |
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