What are the different kernels functions in SVM ?
The basic idea is that when a data set is inseparable in the current dimensions, add another dimension , maybe that way the data will be separable. Just think about it, the example above is in 2D and it is inseparable, but maybe in 3D there is a gap between the apples and the lemons, maybe there is a level difference, so lemons are on level one and lemons are on level two. In this case we can easily draw a separating hyperplane (in 3D a hyperplane is a plane) between level 1 and 2.
Popular kernels are: Polynomial Kernel, Gaussian Kernel, Radial Basis Function (RBF), Laplace RBF Kernel, Sigmoid Kernel, Anove RBF Kernel , etc