The heart of an SVM algorithm is kernel methods. Most kernel algorithms are based on optimization in a convex space, and are statistically well-founded. Kernel stands for the core, or the germ in a fruit. Kernel methods operate using what is called the ‘kernel trick’. This trick involves to compute and work with the inner products of only the relevant pairs of data in the feature space; they do no need to compute all the data in a high-dimensional feature space. The kernel trick makes the algorithm much less demanding in computational and memory resources.
