Rüger, Stefan
(1998).
| DOI (Digital Object Identifier) Link: | http://doi.org/10.1007/PL00013830 |
|---|---|
| Google Scholar: | Look up in Google Scholar |
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: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Item ID: | 11955 |
| Depositing User: | Users 8580 not found. |
| Date Deposited: | 08 Oct 2008 13:13 |
| Last Modified: | 02 Aug 2016 13:18 |
| URI: | http://oro.open.ac.uk/id/eprint/11955 |
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