A Modified Information Criterion for Automatic Model and Parameter Selection in Neural Network Learning
Summary :
This paper proposes a practical training algorithm for artificial neural networks, by which both the optimally pruned model and the optimally trained parameter for the minimum prediction error can be found simultaneously. In the proposed algorithm, the conventional information criterion is modified into a differentiable function of weight parameters, and then it is minimized while being controlled back to the conventional form. Since this method has several theoretical problems, its effectiveness is examined by computer simulations and by an application to practical ultrasonic image reconstruction.
- Publication
- IEICE TRANSACTIONS on Information Vol.E78-D No.4 pp.490-499
- Publication Date
- 1995/04/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Bio-Cybernetics and Neurocomputing
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Sumio WATANABE, "A Modified Information Criterion for Automatic Model and Parameter Selection in Neural Network Learning" in IEICE TRANSACTIONS on Information,
vol. E78-D, no. 4, pp. 490-499, April 1995, doi: .
Abstract: This paper proposes a practical training algorithm for artificial neural networks, by which both the optimally pruned model and the optimally trained parameter for the minimum prediction error can be found simultaneously. In the proposed algorithm, the conventional information criterion is modified into a differentiable function of weight parameters, and then it is minimized while being controlled back to the conventional form. Since this method has several theoretical problems, its effectiveness is examined by computer simulations and by an application to practical ultrasonic image reconstruction.
URL: https://global.ieice.org/en_transactions/information/10.1587/e78-d_4_490/_p
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@ARTICLE{e78-d_4_490,
author={Sumio WATANABE, },
journal={IEICE TRANSACTIONS on Information},
title={A Modified Information Criterion for Automatic Model and Parameter Selection in Neural Network Learning},
year={1995},
volume={E78-D},
number={4},
pages={490-499},
abstract={This paper proposes a practical training algorithm for artificial neural networks, by which both the optimally pruned model and the optimally trained parameter for the minimum prediction error can be found simultaneously. In the proposed algorithm, the conventional information criterion is modified into a differentiable function of weight parameters, and then it is minimized while being controlled back to the conventional form. Since this method has several theoretical problems, its effectiveness is examined by computer simulations and by an application to practical ultrasonic image reconstruction.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - A Modified Information Criterion for Automatic Model and Parameter Selection in Neural Network Learning
T2 - IEICE TRANSACTIONS on Information
SP - 490
EP - 499
AU - Sumio WATANABE
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 1995
AB - This paper proposes a practical training algorithm for artificial neural networks, by which both the optimally pruned model and the optimally trained parameter for the minimum prediction error can be found simultaneously. In the proposed algorithm, the conventional information criterion is modified into a differentiable function of weight parameters, and then it is minimized while being controlled back to the conventional form. Since this method has several theoretical problems, its effectiveness is examined by computer simulations and by an application to practical ultrasonic image reconstruction.
ER -
English
