Enum SVM.KernelTypes

SVM kernel type

public enum SVM.KernelTypes

Fields

Chi2 = 4: Exponential Chi2 kernel, similar to the RBF kernel: \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$.
Custom = -1: Returned by SVM::getKernelType in case when custom kernel has been set
Inter = 5: Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$.
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$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$.