Modeling Based on Smooth Support Vector Regression with ICA Feature Extraction

DOI

10.2991/wartia-17.2017.64How to use a DOI?

Keywords

Smooth support vector regression; Feature extraction; Independent component analysis; Quadratic optimization;

Abstract

Smooth Support Vector Regression (SSVR) is new modified edition of traditional support vector regression for better performance. To further improve the modeling capability of SSVR, it is necessary to take into account the feature extraction based on Independent Component Analysis (ICA) before SSVR. Simulation on the example of function approximation shows that the result of SSVR based on ICA feature extraction is better than that of SSVR without ICA preprocess.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xu-Sheng Gan
AU  - Jun Han
AU  - Zhi-bin Chen
PY  - 2017/11
DA  - 2017/11
TI  - Modeling Based on Smooth Support Vector Regression with ICA Feature Extraction
BT  - Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)
PB  - Atlantis Press
SP  - 331
EP  - 335
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-17.2017.64
DO  - 10.2991/wartia-17.2017.64
ID  - Gan2017/11
ER  -