Analysis and test of efficient methods for building recursive deterministic perceptron neural networks.

Date
2007-12-01
Authors
Elizondo, David
Birkenhead, Ralph, 1955-
Gongora, Mario Augusto
Luyima, P.
Taillard, Eric
Journal Title
Journal ISSN
ISSN
0893-6080
Volume Title
Publisher
Neural Networks
Peer reviewed
Yes
Abstract
Description
This paper introduces a comparison study of three existing methods for building Recursive Deterministic Perceptron Neural Networks. Three methods were compared in terms of their level of generalisation, convergence time and topology sizes. Prior to this study only an exhaustive, NP-Complete method was used for building RDP neural networks. Due to its high complexity, this limited its use in real world classification problems. This work shows that the other two methods, with a polynomial time complexity, can be used as an alternative. These results will widen the use of the RDP neural network. The impact factor is 2.000.
Keywords
RAE 2008, UoA 23 Computer Science and Informatics, recursive deterministic perceptron, batch learning, incremental learning, modular learning, performance sensitivity analysis, convergence time, topology
Citation
Elizondo, D.A. et al. (2007) Analysis and test of efficient methods for building recursive deterministic perceptron neural networks. Neural Networks, 20 (10), pp. 1095-1108.
Research Institute
Institute of Artificial Intelligence (IAI)