A Practical Parameters Selection Method for SVM

Yongsheng Zhu, Chun Hung Li, Youyun Zhang

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

5 Citations (Scopus)


The performance of Support Vector Machine (SVM) is significantly affected by model parameters. One commonly used parameters selection method of SVM, Grid search (GS) method, is very time consuming. Present paper introduces Uniform Design (UD) and Support Vector Regression (SVR) method to reduce the computation cost of traditional GS method: the error bounds of SVM are only computed on some nodes that are selected by UD method, then a Support Vector Regression (SVR) are trained by the computation results. Subsequently, the values of error bound of SVM on other nodes are estimated by the SVR function and the optimized parameters can be selected based on the estimated results. Experiments on seven standard datasets show that parameters selected by proposed method can result in similar test error rate as that obtained by conventional GS method, while the computation cost can be reduced at most from o( n m) to o(n), where m is the number of parameters, n is the number of levels of each parameter.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2004
Subtitle of host publicationInternational Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part I
EditorsFu Liang Yin, Jun Wang, Chengan Guo
PublisherSpringer Berlin Heidelberg
Number of pages6
ISBN (Electronic)9783540286479
ISBN (Print)9783540228417
Publication statusPublished - 11 Aug 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

User-Defined Keywords

  • Support Vector Machine
  • Support Vector Regression
  • Support Vector Machine Classifier
  • Uniform Design
  • Support Vector Machine Regression


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