IEICE TRANSACTIONS on Information
Midpoint-Validation Method for Support Vector Machine Classification
Summary :
In this paper, we propose a midpoint-validation method which improves the generalization of Support Vector Machine. The proposed method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.7 pp.2095-2098
- Publication Date
- 2008/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.7.2095
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Keyword
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Hiroki TAMURA, Koichi TANNO, "Midpoint-Validation Method for Support Vector Machine Classification" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 2095-2098, July 2008, doi: 10.1093/ietisy/e91-d.7.2095.
Abstract: In this paper, we propose a midpoint-validation method which improves the generalization of Support Vector Machine. The proposed method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.2095/_p
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@ARTICLE{e91-d_7_2095,
author={Hiroki TAMURA, Koichi TANNO, },
journal={IEICE TRANSACTIONS on Information},
title={Midpoint-Validation Method for Support Vector Machine Classification},
year={2008},
volume={E91-D},
number={7},
pages={2095-2098},
abstract={In this paper, we propose a midpoint-validation method which improves the generalization of Support Vector Machine. The proposed method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed method.},
keywords={},
doi={10.1093/ietisy/e91-d.7.2095},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Midpoint-Validation Method for Support Vector Machine Classification
T2 - IEICE TRANSACTIONS on Information
SP - 2095
EP - 2098
AU - Hiroki TAMURA
AU - Koichi TANNO
PY - 2008
DO - 10.1093/ietisy/e91-d.7.2095
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - In this paper, we propose a midpoint-validation method which improves the generalization of Support Vector Machine. The proposed method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed method.
ER -
English
