text: In machine learning, this term describes a set of methods that allow operation in high-dimensional implicit feature spaces by replacing inner products with other functions that satisfy Mercer's inequality.
full_question: In machine learning, this term describes a set of methods that allow operation in high-dimensional implicit feature spaces by replacing inner products with other functions that satisfy Mercer's inequality. One example of a set of such functions is the "radial basis function" one of these, which is used in SVM. In mathematics, for an integral transform of a function f, this is a 2D-function that multiplies f inside the integral. For a group homomorphism, the first isomorphism theorem guarantees that an object described by this term is a subgroup of the domain group G. For linear maps, the set described by this term is equivalent to the null space, and is a subspace of the domain whose elements all map to zero in the image. For 10 points, name this term also describing the central program of an operating system.
first_sentence: In machine learning, this term describes a set of methods that allow operation in high-dimensional implicit feature spaces by replacing inner products with other functions that satisfy Mercer's inequality.
raw_answer: kernel