Asymptotic behaviors of support vector machines with Gaussian kernel
Asymptotic behaviors of support vector machines with Gaussian kernel
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DOI：
10.1162/089976603321891855
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发表时间：
2003-07-01
影响因子：
2.9
通讯作者：
Lin, CJ
中科院分区：
文献类型：
--
作者：
Keerthi, SS;Lin, CJ
具有高斯（RBF）核的支持向量机（SVM）在实际应用中很受欢迎。这类支持向量机的模型选择涉及两个超参数：惩罚参数C和核宽度sigma。这封信分析了当这些超参数取非常小或非常大的值时SVM分类器的行为。我们的研究结果有助于理解超参数空间，导致一个有效的启发式方法搜索超参数值与小的泛化误差。分析还表明，如果使用高斯核进行了完整的模型选择，就没有必要考虑线性SVM。
Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.