[1]LI Su,YUAN Zhigao,WANG Cong,et al.Optimization of support vector machine parameters based on group intelligence algorithm[J].CAAI Transactions on Intelligent Systems,2018,13(1):70-84.[doi:10.11992/tis.201707011]
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Optimization of support vector machine parameters based on group intelligence algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 1 Page number: 70-84 Column: 综述 Public date: 2018-01-24
Title:
Optimization of support vector machine parameters based on group intelligence algorithm
Author(s):
LI Su1;  YUAN Zhigao1;  WANG Cong2;  CHEN Tianen2;  GUO Zhaochun1
1. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China;
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Keywords:
support vector machine; statistical study; group intelligence algorithm; optimization of parameters; global optimization; parallel search; convergence speed; optimization accuracy
CLC:
TP181
DOI:
10.11992/tis.201707011
Abstract:
The support vector machine is based on statistical learning theory, which is complete, but problems remain in the application of model parameters, which are difficult to choose. In this paper, we first introduce the basic concepts of the support vector machine and the group intelligence algorithm. Then, to optimize the latest research results and summarize existing problems and solutions, we systematically describe various classical group intelligence algorithms that the support vector machine parameters identified. Finally, drawing on the current research situation for this field, we identify the problems that must be addressed in the optimization of support vector machine parameters in the group intelligence algorithm and outline the prospects for future development trends and research directions.
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