智能系统学报2018，Vol.13Issue(1)：70-84,15.DOI:10.11992/tis.201707011
群智能算法优化支持向量机参数综述
Optimization of support vector machine parameters based on group intelligence algorithm
摘要
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 sum-marize 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 in-telligence algorithm and outline the prospects for future development trends and research directions.关键词
支持向量机/统计学习/群智能/参数优化/全局寻优/并行搜索/收敛速度/寻优精度Key words
support vector machine/statistical study/group intelligence algorithm/optimization of parameters/global optimization/parallel search/convergence speed/optimization accuracy分类
信息技术与安全科学引用本文复制引用
李素,袁志高,王聪,陈天恩,郭兆春..群智能算法优化支持向量机参数综述[J].智能系统学报,2018,13(1):70-84,15.基金项目
国家自然科学基金项目(31101088,91546112) （31101088,91546112）
北京市教育委员会科技计划面上项目(KM201310011010). （KM201310011010）
