The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
支持向量机(SVM)在脑电(EEG)分类中效果较好,其参数寻优方法直接关系着分类的准确率和所需时间。为了探索参数寻优对分类效果的影响,本文采用了固定参数寻优、直接寻优、网格寻优、遗传算法(GA)寻优和粒子群优化算法(PSO)寻优五种参数...支持向量机(SVM)在脑电(EEG)分类中效果较好,其参数寻优方法直接关系着分类的准确率和所需时间。为了探索参数寻优对分类效果的影响,本文采用了固定参数寻优、直接寻优、网格寻优、遗传算法(GA)寻优和粒子群优化算法(PSO)寻优五种参数寻优方法,以BCI Competition IV data 2b数据集进行实验测试,对带通滤波后的数据进行瞬时能量特征的提取,利用五种寻优的参数分类器,得到了9名被试者4~7 s时间内数据的分类准确率和分类所需时间。在用网格寻优和粒子群寻优的分类下,被试S4和被试S8的准确率分别高达96.875%和88.125%,用时最短为3.059 s。直接寻优和固定参数方法的准确率虽低,但分类用时仅为0.002 s和1.305 s,实时性上,更加适合于应用到在线系统中。展开更多
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
文摘支持向量机(SVM)在脑电(EEG)分类中效果较好,其参数寻优方法直接关系着分类的准确率和所需时间。为了探索参数寻优对分类效果的影响,本文采用了固定参数寻优、直接寻优、网格寻优、遗传算法(GA)寻优和粒子群优化算法(PSO)寻优五种参数寻优方法,以BCI Competition IV data 2b数据集进行实验测试,对带通滤波后的数据进行瞬时能量特征的提取,利用五种寻优的参数分类器,得到了9名被试者4~7 s时间内数据的分类准确率和分类所需时间。在用网格寻优和粒子群寻优的分类下,被试S4和被试S8的准确率分别高达96.875%和88.125%,用时最短为3.059 s。直接寻优和固定参数方法的准确率虽低,但分类用时仅为0.002 s和1.305 s,实时性上,更加适合于应用到在线系统中。
