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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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Papers overview

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Highly Cited
2013
Highly Cited
2013
Over the past few decades, many techniques have been developed for the log e valuation of organic-rich rocks (ORR). More recently… 
2010
2010
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
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
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications… 
Highly Cited
2007
2006
2006
A variety of real-world applications heavily relies on the analysis of transient data streams. Due to the rigid processing… 
2006
2006
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
The authors propose a method of direction of arrival (DOA) estimation of signals in the presence of noise whose covariance matrix… 
1985
1985
Abstract : Kernel density estimators are one technique for producing nonparametric estimates of a sample's underlying probability…