Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy

Haochen Shi, Haipeng Xiao, Jianjiang Zhou, Ning Li*, Huiyu Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages383-388
Number of pages6
ISBN (Electronic)9781728101200
DOIs
Publication statusPublished - 03 Jan 2019
Externally publishedYes
Event5th International Conference on Systems and Informatics, ICSAI 2018 - Nanjing, China
Duration: 10 Nov 201812 Nov 2018

Publication series

Name2018 5th International Conference on Systems and Informatics, ICSAI 2018

Conference

Conference5th International Conference on Systems and Informatics, ICSAI 2018
CountryChina
CityNanjing
Period10/11/201812/11/2018

Bibliographical note

Funding 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.

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • 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

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