enum cv::ml::SVM::KernelTypes

Overview

SVM kernel type More…

#include <ml.hpp>

enum KernelTypes
{
    CUSTOM  =-1,
    LINEAR  =0,
    POLY    =1,
    RBF     =2,
    SIGMOID =3,
    CHI2    =4,
    INTER   =5,
};

Detailed Documentation

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.

Enum Values

CUSTOM

Returned by SVM::getKernelType in case when custom kernel has been set

LINEAR

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

POLY

Polynomial kernel: \(K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\).

RBF

Radial basis function (RBF), a good choice in most cases. \(K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\).

SIGMOID

Sigmoid kernel: \(K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\).

CHI2

Exponential Chi2 kernel, similar to the RBF kernel: \(K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\).

INTER

Histogram intersection kernel. A fast kernel. \(K(x_i, x_j) = min(x_i,x_j)\).