Type of a SVM kernel.

Default value is 'RBF'. One of the following predefined kernels:

• Custom Returned by property in case when custom kernel has been set. See cv.SVM.setCustomKernel.
• Linear 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' * x_j.
• Poly Polynomial kernel. K(x_i,x_j) = (gamma * x_i' * x_j + coef0)^degree, gamma>0.
• RBF Radial basis function (RBF), a good choice in most cases. K(x_i,x_j) = exp(-gamma * ||x_i - x_j||^2), gamma>0.
• Sigmoid Sigmoid kernel. K(x_i,x_j) = tanh(gamma * x_i' * x_j + coef0).
• Chi2 Exponential Chi2 kernel, similar to the RBF kernel. K(x_i,x_j) = exp(-gamma * X2(x_i,x_j)), X2(x_i,x_j) = (x_i - x_j)^2 / (x_i + x_j), gamma>0.
• Intersection Histogram intersection kernel. A fast kernel. K(x_i,x_j) = min(x_i,x_j).