群智能算法优化支持向量机参数综述
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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.
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.
引文
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