where K 0 (·) is a kernel function, is the bandwidth, n is the sample size, and x i is the i th observation. The KERNEL option provides three kernel functions (K 0): normal, quadratic, and triangular.

Purpose: This study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how ...

In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, ...

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those ...

1 School of Electrical Engineering, Northeast Electric Power University, Jilin City, China 2 China Electric Power Research Institute, Nanjing, China To accurately evaluate the influence of the ...

A kernel density curve may follow the shape of the distribution more closely. To construct a normal kernel density curve, one parameter is required: the bandwidth .The value of determines the degree ...

Abstract: Kernel density estimation (KDE) models a discrete sample of data as a continuous distribution, supporting the construction of visualizations such as violin plots, heatmaps, and contour plots ...

We propose kernel density decision trees (KDDTs), a novel fuzzy decision tree (FDT) formalism based on kernel density estimation that improves the generalization and robustness of decision trees and ...