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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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Related topics

Related topics

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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… 
Highly Cited
2012
Highly Cited
2012
An affinity-based new local distance function and similarity measure for kNN algorithm
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
2010
Highly Cited
2010
Adaptive Noise Variance Estimation in BayesShrink
A method of noise variance estimation in BayesShrink image denoising is presented. The proposed approach competes with the well… 
Highly Cited
2010
Highly Cited
2010
Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications… 
Highly Cited
2007
Highly Cited
2007
A multi-matcher for ear authentication
2006
2006
Towards Kernel Density Estimation over Streaming Data
A variety of real-world applications heavily relies on the analysis of transient data streams. Due to the rigid processing… 
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… 
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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