Abstract: Automated crowd counting has emerged as a vision-based measurement method for crowd analysis and management. However, current methods based on density maps still suffer from challenges ...

We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models. We pair building footprint and height ...

Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...

Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

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 KDE plots ...

Abstract: Traffic density estimation plays a crucial role in traffic management. This paper introduces a novel approach for accurately estimating traffic density using a graph-based density estimation ...

Density estimation methods often involve kernels, but there are advantages to using splines. Especially if the shape of the density is known to be decreasing, or unimodal, or bimodal, or if the ...

Non parametric Kernel Density Estimator / Classifier. Allows user to input bandwidth but does not find it. Can classify for N dimensions, but can only plot class / decision boundaries for 2.

Umama Ali spent more time as a kid arguing with his brother over who caused the most chaos in GTA Vice City than doing homework, and he’s been unapologetically hooked on games ever since. That ...

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, ...