Anastasiadis, A.D. and Magoulas, George and Vrahatis, M.N. (2005) Sign-based learning schemes for pattern classification. Pattern Recognition Letters 26 (12), pp. 1926-1936. ISSN 0167-8655.
Abstract
This paper introduces a new class of sign-based training algorithms for neural networks that combine the sign-based updates of the Rprop algorithm with the composite nonlinear Jacobi method. The theoretical foundations of the class are described and a heuristic Rprop-based Jacobi algorithm is empirically investigated through simulation experiments in benchmark pattern classification problems. Numerical evidence shows that this new modification of the Rprop algorithm exhibits improved learning speed in all cases tested, and compares favorably against the Rprop and a recently proposed modification, the improved Rprop.
Metadata
| Item Type: | Article |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 22 Jun 2021 12:46 |
| Last Modified: | 09 Aug 2023 12:51 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/44842 |
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