ml_SVM_KernelTypes
import { ml_SVM_KernelTypes } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods.
%SVM kernel type
A comparison of different kernels on the following 2D test case with four classes. Four
SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three
different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score.
Bright means max-score \> 0, dark means max-score \< 0.
Constructors and members
static CUSTOM
Returned by SVM::getKernelType in case when custom kernel has been set
CUSTOM: ml_SVM_KernelTypesValue<-1>,static 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^T x_j.
LINEAR: ml_SVM_KernelTypesValue<0>,static POLY
Polynomial kernel:
K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0.
POLY: ml_SVM_KernelTypesValue<1>,static RBF
Exponential Chi2 kernel, similar to the RBF kernel:
RBF: ml_SVM_KernelTypesValue<2>,static SIGMOID
Sigmoid kernel: K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0).
SIGMOID: ml_SVM_KernelTypesValue<3>,static CHI2
Exponential Chi2 kernel, similar to the RBF kernel:
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.
CHI2: ml_SVM_KernelTypesValue<4>,static INTER
Histogram intersection kernel. A fast kernel. K(x_i, x_j) = min(x_i,x_j).
INTER: ml_SVM_KernelTypesValue<5>These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.