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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, propomos um método de validação de ponto médio que melhora a generalização da Máquina de Vetores de Suporte. O método proposto cria dados de ponto médio, bem como um parâmetro de ajuste de giro da Support Vector Machine usando dados de ponto médio e dados de treinamento anteriores. Comparamos seu desempenho com a Máquina de Vetores de Suporte original, Perceptron Multicamada, Rede Neural de Função de Base Radial e também testamos nosso método proposto em vários problemas de benchmark. Os resultados obtidos na simulação mostram a eficácia do método proposto.
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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},}
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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 -