A function that helps to separate datasets that can't be separated with a classic linear approach.

A kernel function implicitly finds a higher dimensional space in which a non linearly separable dataset can be easily separated, then project the separation back to the original dataset space.

Technically, a kernel function allows making complex non-linear classifiers using any learning algorithm that can be expressed solely in terms of dot products between two vectors.

Back to Machine Learning Terms