理解SVM的各个参数是什么

最新推荐文章于 2024-01-05 09:48:07 发布

MelonEater

最新推荐文章于 2024-01-05 09:48:07 发布

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分类专栏：机器学习文章标签：机器学习 SVM OPENCV

本文链接：https://blog.youkuaiyun.com/waterMelon_eater/article/details/90452093

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本文详细介绍了SVM的三种类型：C-SVC、ν-SVC和One-Class SVM，以及它们在分类和回归问题中的应用。同时，讨论了SVM的四种核函数，包括线性、多项式、径向基函数（RBF）和Sigmoid，并提供了每种核函数的数学表达式。此外，文章还讲解了SVM参数如GAMMA、COEF、DEGREE、C、P和NU的含义及影响，帮助读者理解如何调整这些参数以优化模型性能。

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SVM Types

分类
C_SVC=100:
C-Support Vector Classification. n-class classification (n $\geq$ 2), allows imperfect separation of classes with penalty multiplier C for outliers.

NU_SVC=101:
$\nu$ -Support Vector Classification. n-class classification with possible imperfect separation. Parameter $\nu$ (in the range 0…1, the larger the value, the smoother the decision boundary) is used instead of C.

单类界限
ONE_CLASS=102:
Distribution Estimation (One-class %SVM). All the training data are from the same class, %SVM builds a boundary that separates the class from the rest of the feature space.

回归
EPS_SVR=103:
$\epsilon$ -Support Vector Regression. The distance between feature vectors from the training set and the fitting hyper-plane must be less than p. For outliers the penalty multiplier C is used.

NU_SVR=104:
$\nu$ -Support Vector Regression. $\nu$ is used instead of p.

SVM KernelType

CUSTOM=-1:
Returned by SVM::getKernelType in case when custom kernel has been set

LINEAR=0:
Linear kernel. No mapping is done, linear discrimination (or regression) is done in the original feature space. It is the fastest option. $K(x_i, x_j) = x_i^T x_j$ .