Machine Learning, Kernel, Hilbert Space, Mercer Theorem.
example is the Gaussian kernel. Given two vectors, the similarity will diminish with the radius of σ. The success of learning with kernels depends very strongly on the choice of kernel. It is often problem-specific. Kernels act as a shortcut as neither there is a need to compute higher-dimensional