Levenberg-Marquardt and Conjugate Gradient Methods Applied to a High-order Neural Network
Abstract
The HONEST network is a high order neural network that uses product units and adaptable exponential weights. In this paper, we explore the use of several learning methods with the HONEST network: Levenberg-Marquardt (LM), Conjugate Gradient (CG), Scaled Conjugate Gradient (a technique that combines LM and CG), and resilient propagation (RP). Using a benchmark of 19 datasets, we find that the first three methods mentioned produce lower average test set errors than RP to a statistically significant extent.
Recommended Citation
I. El-Nabarawy et al., "Levenberg-Marquardt and Conjugate Gradient Methods Applied to a High-order Neural Network," Proceedings of International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2013.
The definitive version is available at https://doi.org/10.1109/IJCNN.2013.6707004
Meeting Name
2013 International Joint Conference on Neural Networks (IJCNN) (2013: Aug. 4-9, Dallas, TX)
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-1467361293; 978-1467361286
International Standard Serial Number (ISSN)
2161-4393
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2013 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2013
