r/MachineLearning - [D] List of DL topics with resources for a quick brief, especially before interviews

The kernel trick can be used with any algorithm from the broad class of algorithms known as kernel machines. The most popular kernel machines are the support vector machine and logistic regression. Essentially, the optimisation objective of a generic kernel machine is formulated in such a way that it depends only on dot products between input vectors. This allows us to swap these dot products with a kernel computation of the dot product into some higher-dimensional (possibly infinite dimensional) space. The key to a kernel function is that it MUST have the following property: K(x_i x_j) g(x_i), g(x_j) for some g.