An algorithm of Model Selection for Support Vector Regression

DOI

10.2991/iccasm.2012.24How to use a DOI?

Keywords

support vector regression (SVR), model selection, gradient descent, Riemannian geometry

Abstract

To solve the problem of SVR (support vector regression) model selection, this paper proposed a SVM (support vector machine) model parameter optimization algorithm based on gradient descent algorithm. The algorithm obtained the local optimal model parameter by minimizing the model evaluation criteria over the parameter set. Then on the basis of Riemannian geometry, a conformal transformation suitable for SVR was proposed which corrected kernel function in a data-based way. This algorithm can further enhance the generalization ability of SVR. The simulated results are illustrated to show the feasibility and effectiveness of the algorithm.

Copyright

© 2012, 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  - Xuesi Li
AU  - Hongqiao Yang
AU  - Jing Sun
AU  - Yangang Bi
AU  - Yuanli Wu
PY  - 2012/08
DA  - 2012/08
TI  - An algorithm of Model Selection for Support Vector Regression
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 96
EP  - 99
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.24
DO  - 10.2991/iccasm.2012.24
ID  - Li2012/08
ER  -