Turn Excel into a lightweight data-science tool for cleaning datasets, standardizing dates, visualizing clusters, and ...

Personally identifiable information has been found in DataComp CommonPool, one of the largest open-source data sets used to train image generation models. Millions of images of passports, credit cards ...

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 ...

Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

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 ...

An intelligent power transducer plays an important role in the acquisition, monitoring, and control of data in power systems. A power transducer faces challenges of reduced measurement accuracy and ...

AKDE provides an accurate, adaptive kernel density estimator based on the Gaussian Mixture Model for multidimensional data. This Python implementation includes automatic grid construction for ...

Researchers at the University of Waterloo’s Cheriton School of Computer Science in Canada found that modifying just 30 lines of code in the Linux kernel could cut data center energy consumption by 30% ...

University of Florida scientists have statistically analyzed large amounts of data collected by Burmese python contractors, revealing critical insights about how to most efficiently remove the ...

Abstract: This paper shows that adaptive kernel density estimator (KDE) can be derived effectively from Isolation Kernel. Existing adaptive KDEs often employ a data independent kernel such as Gaussian ...

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