Jeng Jin-Tsong
IEEE Trans Syst Man Cybern B Cybern. 2006 Jun;36(3):699-709. doi: 10.1109/tsmcb.2005.861067.
To select the hyperparameters of the support vector machine for regression (SVR), a hybrid approach is proposed to determine the kernel parameter of the Gaussian kernel function and the epsilon value of Vapnik's epsilon-insensitive loss function. The proposed hybrid approach includes a competitive agglomeration (CA) clustering algorithm and a repeated SVR (RSVR) approach. Since the CA clustering algorithm is used to find the nearly "optimal" number of clusters and the centers of clusters in the clustering process, the CA clustering algorithm is applied to select the Gaussian kernel parameter. Additionally, an RSVR approach that relies on the standard deviation of a training error is proposed to obtain an epsilon in the loss function. Finally, two functions, one real data set (i.e., a time series of quarterly unemployment rate for West Germany) and an identification of nonlinear plant are used to verify the usefulness of the hybrid approach.
为了选择支持向量机回归（SVR）的超参数，提出了一种混合方法来确定高斯核函数的核参数和Vapnik的ε-不敏感损失函数的ε值。所提出的混合方法包括竞争凝聚（CA）聚类算法和重复SVR（RSVR）方法。由于CA聚类算法用于在聚类过程中找到近似“最优”的聚类数和聚类中心，因此将CA聚类算法应用于选择高斯核参数。此外，还提出了一种基于训练误差标准差的RSVR方法来获得损失函数中的ε。最后，使用两个函数、一个真实数据集（即西德季度失业率的时间序列）和一个非线性工厂的识别来验证该混合方法的有效性。
