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

作　　者：李素[1] 袁志高 王聪 陈天恩[2] 郭兆春 

机构地区：[1]北京工商大学食品安全大数据技术北京市重点实验室,北京100048 [2]国家农业信息化工程技术研究中心,北京100097

出　　处：《智能系统学报》2018年第1期70-84,共15页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金项目(31101088;91546112);北京市教育委员会科技计划面上项目(KM201310011010)

摘　　要：支持向量机建立在统计学习的理论基础之上,具有理论的完备性,但是在应用上仍然存在模型参数难以选择的问题。首先,介绍了支持向量机和群智能算法的基本概念;然后,系统地叙述了各种经典的群智能算法进行支持向量机参数优化取得的最新研究成果以及总结了优化过程中存在的问题和解决方案;最后,结合该领域当前研究现状,提出了群智能算法优化支持向量机参数研究中需要关注的问题,展望了这一研究方向在未来的发展趋势和前景。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.

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 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]