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/).
Cite this article
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 -