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Kernel density estimation

Known as: Kernel density estimate, Kernel density, Parzen Windows 
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel… 
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AnalyticaApache SparkC++Cluster analysis

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2013
Highly Cited
2013
Determination of Formation Organic Carbon Content Using a New Neutron-Induced Gamma Ray Spectroscopy Service that Directly Measures Carbon
Over the past few decades, many techniques have been developed for the log e valuation of organic-rich rocks (ORR). More recently… 
2013
2013
Age Estimation Using Local Binary Pattern Kernel Density Estimate
We propose a novel kernel method for constructing local binary pattern statistics for facial representation in human age… 
2010
2010
Online Discriminative Kernel Density Estimation
We propose a new method for online estimation of probabilistic discriminative models. The method is based on the recently… 
Highly Cited
2007
Highly Cited
2007
A multi-matcher for ear authentication
2006
2006
MAI-Free MC-CDMA Systems Based on Hadamard–Walsh Codes
It is known that multicarrier code-division multiple-access (MC-CDMA) systems suffer from multiaccess interference (MAI) when the… 
2006
2006
Very fast optimal bandwidth selection for univariate kernel density estimation
Most automatic bandwidth selection procedures for kernel density estimates require estimation of quantities involving the density… 
1998
1998
Facial Memory Is Kernel Density Estimation (Almost)
We compare the ability of three exemplar-based memory models, each using three different face stimulus representations, to… 
1998
1998
A Modelling Approach for Bandwidth Selection in Kernel Density Estimation
A new procedure is proposed for bandwidth selection in univariate kernel density estimation. Rather than concentrate on… 
Highly Cited
1992
Highly Cited
1992
Estimation of the directions of arrival of signals in unknown correlated noise. I. The MAP approach and its implementation
The authors propose a method of direction of arrival (DOA) estimation of signals in the presence of noise whose covariance matrix… 
1985
1985
Introduction to Kernel Density Estimation.
Abstract : Kernel density estimators are one technique for producing nonparametric estimates of a sample's underlying probability… 
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