Smithies, Rob and Salhi, Said and Queen, Nat M. (2004) Adaptive Hybrid Learning for Neural Networks. Neural Computation, 16 (1). pp. 139-157. ISSN 0899-7667. (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)
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.
|Subjects:||H Social Sciences|
|Divisions:||Faculties > Social Sciences > Kent Business School > Management Science|
|Depositing User:||Said Salhi|
|Date Deposited:||17 Oct 2008 09:58|
|Last Modified:||07 Apr 2014 13:10|
|Resource URI:||https://kar.kent.ac.uk/id/eprint/5267 (The current URI for this page, for reference purposes)|