 | SVMKernelTypes Enumeration |
SVM kernel type
Namespace: OpenCvSharp.ML
Assembly: OpenCvSharp (in OpenCvSharp.dll) Version: 1.0.0
SyntaxMembers| Member name | Value | Description | |
|---|---|---|---|
| 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. \f$K(x_i, x_j) = x_i^T x_j\f$. | |
| Poly | 1 | Polynomial kernel: \f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. | |
| Rbf | 2 | Radial basis function (RBF), a good choice in most cases. \f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. | |
| Sigmoid | 3 | Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. | |
| Chi2 | 4 | Exponential Chi2 kernel, similar to the RBF kernel: \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. | |
| Inter | 5 | Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. |
See Also