Active Learning for Optimal Generalization in Trigonometric Polynomial Models
Summary :
In this paper, we consider the problem of active learning, and give a necessary and sufficient condition of sample points for the optimal generalization capability. By utilizing the properties of pseudo orthogonal bases, we clarify the mechanism of achieving the optimal generalization capability. We also show that the condition does not only provide the optimal generalization capability but also reduces the computational complexity and memory required to calculate learning result functions. Based on the optimality condition, we give design methods of optimal sample points for trigonometric polynomial models. Finally, the effectiveness of the proposed active learning method is demonstrated through computer simulations.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.9 pp.2319-2329
- Publication Date
- 2001/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Algorithms and Data Structures
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Masashi SUGIYAMA, Hidemitsu OGAWA, "Active Learning for Optimal Generalization in Trigonometric Polynomial Models" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 9, pp. 2319-2329, September 2001, doi: .
Abstract: In this paper, we consider the problem of active learning, and give a necessary and sufficient condition of sample points for the optimal generalization capability. By utilizing the properties of pseudo orthogonal bases, we clarify the mechanism of achieving the optimal generalization capability. We also show that the condition does not only provide the optimal generalization capability but also reduces the computational complexity and memory required to calculate learning result functions. Based on the optimality condition, we give design methods of optimal sample points for trigonometric polynomial models. Finally, the effectiveness of the proposed active learning method is demonstrated through computer simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_9_2319/_p
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@ARTICLE{e84-a_9_2319,
author={Masashi SUGIYAMA, Hidemitsu OGAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Active Learning for Optimal Generalization in Trigonometric Polynomial Models},
year={2001},
volume={E84-A},
number={9},
pages={2319-2329},
abstract={In this paper, we consider the problem of active learning, and give a necessary and sufficient condition of sample points for the optimal generalization capability. By utilizing the properties of pseudo orthogonal bases, we clarify the mechanism of achieving the optimal generalization capability. We also show that the condition does not only provide the optimal generalization capability but also reduces the computational complexity and memory required to calculate learning result functions. Based on the optimality condition, we give design methods of optimal sample points for trigonometric polynomial models. Finally, the effectiveness of the proposed active learning method is demonstrated through computer simulations.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Active Learning for Optimal Generalization in Trigonometric Polynomial Models
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2319
EP - 2329
AU - Masashi SUGIYAMA
AU - Hidemitsu OGAWA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2001
AB - In this paper, we consider the problem of active learning, and give a necessary and sufficient condition of sample points for the optimal generalization capability. By utilizing the properties of pseudo orthogonal bases, we clarify the mechanism of achieving the optimal generalization capability. We also show that the condition does not only provide the optimal generalization capability but also reduces the computational complexity and memory required to calculate learning result functions. Based on the optimality condition, we give design methods of optimal sample points for trigonometric polynomial models. Finally, the effectiveness of the proposed active learning method is demonstrated through computer simulations.
ER -
English
