Adaptive hybrid learning for neural networks

Authors:
Rob Smithies;Said Salhi;Nat Queen
Affiliations:
School of Mathematics and Statistics, University of Birmingham, Birmingham B15 2TT, U.K.;School of Mathematics and Statistics, University of Birmingham, Birmingham B15 2TT, U.K.;School of Mathematics and Statistics, University of Birmingham, Birmingham B15 2TT, U.K.
Venue:
Neural Computation
Year:
2004

Citing 4
Cited 0

SuperSAB: fast adaptive back propagation with good scaling properties

Neural Networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning

Neural Networks
Local Search in Combinatorial Optimization

Local Search in Combinatorial Optimization

Quantified Score

Hi-index	0.00

Visualization

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.