Cover's theorem  Cover's theorem is a statement in computational learning theory and is one of the primary theoretical motivations for the use of non-linear kernel methods in machine learning applications. The theorem states that given a set of training data that is not linearly separable, one can with high probability transform it into a training set that is linearly separable by projecting it into a higher-dimensional space via some non-linear transformation. The theorem is named after the information theorist Thomas M. Cover who stated it in 1965. In his own words,   A deterministic mapping may be used: suppose there are formula_1 samples. Lift them onto the vertices of the simplex in the formula_2 dimensional real space. Since every partition of the samples into two sets is separable by a linear separator, the theorem follows.