Incremental Construction of Projection Generalizing Neural Networks
Summary :
In many practical situations in NN learning, training examples tend to be supplied one by one. In such situations, incremental learning seems more natural than batch learning in view of the learning methods of human beings. In this paper, we propose an incremental learning method in neural networks under the projection learning criterion. Although projection learning is a linear learning method, achieving the above goal is not straightforward since it involves redundant expressions of functions with over-complete bases, which is essentially related to pseudo biorthogonal bases (or frames). The proposed method provides exactly the same learning result as that obtained by batch learning. It is theoretically shown that the proposed method is more efficient in computation than batch learning.
- Publication
- IEICE TRANSACTIONS on Information Vol.E85-D No.9 pp.1433-1442
- Publication Date
- 2002/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
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Masashi SUGIYAMA, Hidemitsu OGAWA, "Incremental Construction of Projection Generalizing Neural Networks" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 9, pp. 1433-1442, September 2002, doi: .
Abstract: In many practical situations in NN learning, training examples tend to be supplied one by one. In such situations, incremental learning seems more natural than batch learning in view of the learning methods of human beings. In this paper, we propose an incremental learning method in neural networks under the projection learning criterion. Although projection learning is a linear learning method, achieving the above goal is not straightforward since it involves redundant expressions of functions with over-complete bases, which is essentially related to pseudo biorthogonal bases (or frames). The proposed method provides exactly the same learning result as that obtained by batch learning. It is theoretically shown that the proposed method is more efficient in computation than batch learning.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_9_1433/_p
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@ARTICLE{e85-d_9_1433,
author={Masashi SUGIYAMA, Hidemitsu OGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Incremental Construction of Projection Generalizing Neural Networks},
year={2002},
volume={E85-D},
number={9},
pages={1433-1442},
abstract={In many practical situations in NN learning, training examples tend to be supplied one by one. In such situations, incremental learning seems more natural than batch learning in view of the learning methods of human beings. In this paper, we propose an incremental learning method in neural networks under the projection learning criterion. Although projection learning is a linear learning method, achieving the above goal is not straightforward since it involves redundant expressions of functions with over-complete bases, which is essentially related to pseudo biorthogonal bases (or frames). The proposed method provides exactly the same learning result as that obtained by batch learning. It is theoretically shown that the proposed method is more efficient in computation than batch learning.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Incremental Construction of Projection Generalizing Neural Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1433
EP - 1442
AU - Masashi SUGIYAMA
AU - Hidemitsu OGAWA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2002
AB - In many practical situations in NN learning, training examples tend to be supplied one by one. In such situations, incremental learning seems more natural than batch learning in view of the learning methods of human beings. In this paper, we propose an incremental learning method in neural networks under the projection learning criterion. Although projection learning is a linear learning method, achieving the above goal is not straightforward since it involves redundant expressions of functions with over-complete bases, which is essentially related to pseudo biorthogonal bases (or frames). The proposed method provides exactly the same learning result as that obtained by batch learning. It is theoretically shown that the proposed method is more efficient in computation than batch learning.
ER -
English
