In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in kernel density estimation to estimate random variables' density functions, or in kernel regression to estimate the conditional expectation of a random variable.

What is a sampling kernel?

Kernel density estimation is a well-known method for estimating the unknown probability density distribution based on a given sample [30], [32]. It estimates the unknown density function by averaging over a set of kernel homogeneous functions that are centered at each sample point.

What is a data kernel?

In machine learning, a “kernel” is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem. ... The kernel function is what is applied on each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable.