Referenced Items are not available for this document.
In this paper, we present a new class of quasi-Newton methods for an effective learning in large multilayer perceptron (MLP)-networks. The algorithms introduced in this work, named LQN, utilize an iterative scheme of a generalized BFGS-type method, involving a suitable family of matrix algebras L. The main advantages of these innovative methods are based upon the fact that they have an O(nlogn) complexity per step and that they require O(n) memory allocations. Numerical experiences, performed on a set of standard benchmarks of MLP-networks, show the competitivity of the LQN methods, especially for large values of n.
Published in:
Neural Networks, IEEE Transactions on
(Volume:14
,
Issue:
2
)
Date of Publication: Mar 2003
- Page(s):
- 263 - 273
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 7559861
- Digital Object Identifier :
- 10.1109/TNN.2003.809425
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 26 March 2003
- Issue Date :
- Mar 2003
- Sponsored by :
- IEEE Computational Intelligence Society
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
