Kernel Functions for SVM

In this section, we present the most used SVM kernels. As these functions are usually computed in a high-dimensional space and have a nonlinear character, it is not easy to derive an impression on the shape of the classification hyperplane generated by these kernels. Therefore, we will present several plots for SVM models obtained for the dataset shown in Table 5. This dataset is not separable with a linear classifier, but the two clusters can be clearly distinguished. 

This is a linear classifier, and it should be used as a test of the nonlinearity in the training set, as well as a reference for the eventual classification improvement obtained with nonlinear kernels. 

The polynomial kernel is a simple and efficient method for modeling nonlinear relationships  

Radial basis functions (RBF) are widely used kernels, usually in the Gaussian form  

If discontinuities in the hyperplane are acceptable, an exponential RBF kernel is worth trying  

---

Popular choices of kernel functions for SVMs are as follows Polynomial of degree p with parameters and 2-... 

---