Kernel Density Estimation (KDE) is used in machine learning to estimate the probability density function of a random variable. In practice, KDE is a non-parametric way to estimate the distribution ...

Abstract: In kernel density estimation methods, an approximation of the data probability density function is achieved by locating a kernel function at each data location. The smoothness of the ...

In kernel density estimation methods, an approximation of the data probability density function is achieved by locating a kernel function at each data location. The smoothness of the functional ...