SVM-RBF核下的参数调参准则

最新推荐文章于 2024-11-11 00:30:00 发布

原创最新推荐文章于 2024-11-11 00:30:00 发布 · 3.7k 阅读

2 ·

CC 4.0 BY-SA版权

文章标签：

#svm #kernel

机器学习专栏收录该内容

2 篇文章

订阅专栏

支持向量机(SVM)使用径向基函数(RBF)核时，需调整两个关键参数：C和gamma。C用于平衡训练样本误分类与决策边界简化；gamma定义了单个训练样本的影响范围。适当选择C和gamma对提升SVM性能至关重要。

When training an SVM with the Radial Basis Function (RBF) kernel,
two parameters must be considered: C and gamma.
The parameter C, common to all SVM kernels,
trades off misclassification of training examples against simplicity of the decision surface.
A low C makes the decision surface smooth, while a high C aims at classifying all training examples correctly.
gamma defines how much influence a single training example has. The larger gamma is,
the closer other examples must be to be affected.
Proper choice of C and gamma is critical to the SVM’s performance.
One is advised to use sklearn.model_selection.GridSearchCV with C and gamma spaced exponentially far apart to choose good values.