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Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1
Smoothing Support Vector Machines for e-Insensitive Regressi
Jinan, China
October 16-October 18
ISBN: 0-7695-2528-8
ASCII Text
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Jinzhi Xiong, Tianming Hu, Jinlian Hu, Guangming Li, Hong Peng, "Smoothing Support Vector Machines for e-Insensitive Regressi," Intelligent Systems Design and Applications, International Conference on, vol. 1, pp. 222-228, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1, 2006.
 
 
BibTex
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@article{ 10.1109/ISDA.2006.244,
author = {Jinzhi Xiong and Tianming Hu and Jinlian Hu and Guangming Li and Hong Peng},
title = {Smoothing Support Vector Machines for e-Insensitive Regressi},
journal ={Intelligent Systems Design and Applications, International Conference on},
volume = {1},
year = {2006},
isbn = {0-7695-2528-8},
pages = {222-228},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2006.244},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Systems Design and Applications, International Conference on
TI - Smoothing Support Vector Machines for e-Insensitive Regressi
SN - 0-7695-2528-8
SP222
EP228
A1 - Jinzhi Xiong,
A1 - Tianming Hu,
A1 - Jinlian Hu,
A1 - Guangming Li,
A1 - Hong Peng,
PY - 2006
KW - null
VL - 1
JA - Intelligent Systems Design and Applications, International Conference on
ER -
 
Jinzhi Xiong, Dongguan University of Technology, China
Tianming Hu, Dongguan University of Technology, China
Jinlian Hu, Dongguan University of Technology, China
Guangming Li, Dongguan University of Technology, China
Hong Peng, South China University of Technology, China
Researching smooth support vector machine (SVM) for regression is an active field in data mining. Recently, Lee et al. proposed the smooth SVM for e-insensitive regression, where smoothing functions play a vital role in smooth SVMs. This paper presents a comparative study on three smooth SVMs: smooth SVM, polynomial smooth SVM and smooth e-support vector regression. It also discusses promising directions of e-support vector regression for future work.
Citation:
Jinzhi Xiong, Tianming Hu, Jinlian Hu, Guangming Li, Hong Peng, "Smoothing Support Vector Machines for e-Insensitive Regressi," isda, vol. 1, pp.222-228, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1, 2006
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