What is SVM kernel?

The SVM kernel is a function which takes low dimensional input space and transforms it to a higher dimensional space. It converts not separable problem to separable problem. It is mostly useful in non-linear separation problem. It performs extremely complex data transformations, then finds out the process to separate the data based on the labels or outputs defined. To train a support vector classifier and then optimize the objective function, we must perform some operations with the higher dimensional vectors.