Saad, David and Rattray, Magnus (1998). Learning with regularizers in multilayer neural networks. Physical Review E, 57 (2), pp. 2170-2176.
Abstract
We study the effect of regularization in an on-line gradient-descent learning scenario for a general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labelled by a two-layer teacher network with an arbitrary number of hidden units which may be corrupted by Gaussian output noise. We examine the effect of weight decay regularization on the dynamical evolution of the order parameters and generalization error in various phases of the learning process, in both noiseless and noisy scenarios.
Divisions: | College of Engineering & Physical Sciences > Systems analytics research institute (SARI) |
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Additional Information: | Copyright of the American Physical Society |
Publication ISSN: | 1550-2376 |
Last Modified: | 29 Nov 2023 10:00 |
Date Deposited: | 11 Mar 2019 17:29 |
Full Text Link: | |
Related URLs: |
http://prola.ap ... /v57/i2/p2170_1
(Publisher URL) |
PURE Output Type: | Article |
Published Date: | 1998-02 |
Authors: |
Saad, David
Rattray, Magnus |