Levenberg-Marquardt and Conjugate Gradient methods applied to a high-order neural network
Files
Department
Computer Science & Engineering Department
Description
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. © 2013 IEEE.
Publication Date
12-1-2013
Document Type
Book Chapter
Book Title
Proceedings of the International Joint Conference on Neural Networks
ISBN
SCOPUS_ID:84893614990
Publisher
IEEE
City
Dallas, TX, USA
First Page
2123
Last Page
2129
Recommended Citation
APA Citation
El-Nabarawy, I.
Abdelbar, A.
&
Wunsch, D.
(2013).Levenberg-Marquardt and Conjugate Gradient methods applied to a high-order neural network. IEEE. , 2123-2129
https://fount.aucegypt.edu/faculty_book_chapters/536
MLA Citation
El-Nabarawy, Islam, et al.
Levenberg-Marquardt and Conjugate Gradient methods applied to a high-order neural network. IEEE, 2013.pp. 2123-2129
https://fount.aucegypt.edu/faculty_book_chapters/536