Representer theorem

For computer science, in statistical learning theory, a representer theorem is any of several related results stating that a minimizer ${\displaystyle f^{*}}$ of a regularized empirical risk functional defined over a reproducing kernel Hilbert space can be represented as a finite linear combination of kernel products evaluated on the input points in the training set data.