kernelfunktio
A kernelfunktion, or kernel function, is a function k: X × X → R that measures the similarity between two inputs. In many settings it corresponds to an inner product in a (potentially high‑dimensional) feature space: k(x, y) = φ(x) · φ(y). This interpretation allows nonlinear learning algorithms to operate in implicit high-dimensional spaces by computing kernel evaluations directly in the input space.
Key properties of a kernel include symmetry and positive semidefiniteness. A kernel is symmetric if k(x, y)
Common examples of kernels are the linear kernel k(x, y) = x · y, the polynomial kernel (x ·
Applications span machine learning and statistics. Kernels enable the kernel trick for algorithms such as support
Computationally, kernel methods require forming and manipulating an n × n Gram matrix, with costs growing at