Nesting Differential Evolution to Optimize the Parameters of Support Vector Machine for Gender Classification of Facial Images

DOI

10.2991/isrme-15.2015.103How to use a DOI?

Keywords

Support Vector Machine; Parameter Optimization; Differential Evolution; Nesting Optimization Method; Gender Classification of Facial Images

Abstract

Support Vector Machine (SVM) is an influential and fashionable statistical learning technique for binary classification and regression. The generalization performance of SVM highly depends on proper tuning of the penalty parameter and kernel function parameter(s). An original technique is proposed to quickly search the optimal SVM parameters separately by nesting two differential evolution (DE) algorithms, which can avoid repetitious costly computation and then shrink the computation cost by orders of magnitude compared to the existing approaches which tune all the parameters concurrently. The experimental results on gender classification of facial images illustrate that the proposed technique can efficiently construct an SVM classifier with significant generalization capabilities.

Copyright

© 2015, 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  - Pin Liao
AU  - Sensen Wang
AU  - Xin Zhang
AU  - Kunlun Li
AU  - Mingyan Wang
PY  - 2015/04
DA  - 2015/04
TI  - Nesting Differential Evolution to Optimize the Parameters of Support Vector Machine for Gender Classification of Facial Images
BT  - Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering
PB  - Atlantis Press
SP  - 482
EP  - 485
SN  - 1951-6851
UR  - https://doi.org/10.2991/isrme-15.2015.103
DO  - 10.2991/isrme-15.2015.103
ID  - Liao2015/04
ER  -