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
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.