The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam makalah ini, kami mencadangkan kaedah pengesahan titik tengah yang meningkatkan generalisasi Mesin Vektor Sokongan. Kaedah yang dicadangkan mencipta data titik tengah, serta parameter pelarasan pusingan Mesin Vektor Sokongan menggunakan data titik tengah dan data latihan sebelumnya. Kami membandingkan prestasinya dengan Mesin Vektor Sokongan asal, Multilayer Perceptron, Radial Basis Function Neural Network dan juga menguji kaedah cadangan kami pada beberapa masalah penanda aras. Keputusan yang diperoleh daripada simulasi menunjukkan keberkesanan kaedah yang dicadangkan.
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Salinan
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
Salinan
@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},}
Salinan
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 -