Non linear Classifiers Kernels (e.g. Regularized SVM classifier with RBF kernel) - SoojungHong/MachineLearning GitHub Wiki

How can we use linear classifiers to make non-linear predictions? --> The easiest way to do this is to first map all the examples x to different feature vector phi(x). Then we simply use a linear classifier on the new (higher dimensional) feature vectors, pretending that they were the original input vectors. As a result, all the linear classifiers we have learned remain applicable, yet produce non-linear classifiers in the original coordinates.

Kernel function is the inner product K(x(i), x) = phi(x(i), x). It is a function of two arguments and it is always defined as the inner product of feature vectors corresponding to the input arguments. RBF is radical basis kernel. f(x) = exp(-||x||^2/2)