By analyzing the influences of kernel parameter and penalty factor for generalization performance on Support Vector Machine (SVM), a novel parameter optimization algorithm based on segmented dichotomy is proposed for Radial Basis Function (RBF) kernel. Combine with Segmented Dichotomy(SD) and Gird Searching(GS) method, a composite parameter selection, SD-GS algorithm, is structured for rapid optimization of kernel parameter and penalty factor. UCI Machine Learning database is used to test our proposed method. Experimental results have shown that performance on parameter selection is better than traversal exponential grid searching. Thus, the optimized parameter combination of SD-GS algorithm enables RBF kernel in SVM to have higher generalization performance.
|Title of host publication||2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 03 Jan 2019|
|Event||5th International Conference on Systems and Informatics, ICSAI 2018 - Nanjing, China|
Duration: 10 Nov 2018 → 12 Nov 2018
|Name||2018 5th International Conference on Systems and Informatics, ICSAI 2018|
|Conference||5th International Conference on Systems and Informatics, ICSAI 2018|
|Period||10/11/2018 → 12/11/2018|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (No 20175152036). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education.
© 2018 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- parameter optimization
- RBF kernel
- segmented dichotomy(SD)
- Support Vector Machine (SVM)
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Control and Systems Engineering