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Adaptive Hybrid Learning for Neural Networks

Smithies, Rob, Salhi, Said, Queen, Nat M. (2004) Adaptive Hybrid Learning for Neural Networks. Neural Computation, 16 (1). pp. 139-157. ISSN 0899-7667. (doi:10.1162/08997660460734038) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:5267)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1162/08997660460734038

Abstract

A robust locally adaptive learning algorithm is developed via two enhancements of the Resilient Propagation (RPROP) method. Remaining drawbacks of the gradient-based approach are addressed by hybridization with gradient-independent Local Search. Finally, a global optimization method based on recursion of the hybrid is constructed, making use of tabu neighborhoods to accelerate the search for minima through diversification. Enhanced RPROP is shown to be faster and more accurate than the standard RPROP in solving classification tasks based on natural data sets taken from the UCI repository of machine learning databases. Furthermore, the use of Local Search is shown to improve Enhanced RPROP by solving the same classification tasks as part of the global optimization method.

Item Type: Article
DOI/Identification number: 10.1162/08997660460734038
Subjects: H Social Sciences
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Said Salhi
Date Deposited: 17 Oct 2008 09:58 UTC
Last Modified: 08 Feb 2020 04:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/5267 (The current URI for this page, for reference purposes)
Salhi, Said: https://orcid.org/0000-0002-3384-5240
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