Results 141 to 150 of about 257,461 (188)

Generalized Kernel Density Estimator

Theory of Probability & Its Applications, 2000
Summary: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimators as well as the popular Abramson's estimator. We show that the generalized estimators may perform much better than the classical one if the distribution has a heavy tail.
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Online Discriminative Kernel Density Estimator With Gaussian Kernels

IEEE Transactions on Cybernetics, 2014
We propose a new method for a supervised online estimation of probabilistic discriminative models for classification tasks. The method estimates the class distributions from a stream of data in the form of Gaussian mixture models (GMMs). The reconstructive updates of the distributions are based on the recently proposed online kernel density estimator ...
Matej, Kristan, Ales, Leonardis
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VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES

Australian Journal of Statistics, 1990
SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location.
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Deconvolving kernel density estimators

Statistics, 1990
This paper considers estimation of a continuous bounded probability density when observations from the density are contaminated by additive measurement errors having a known distribution. Properties of the estimator obtained by deconvolving a kernel estimator of the observed data are investigated.
Leonard A. Stefanski, Raymond J. Carroll
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Kernel Density Estimation

2017
This chapter describes the kernel density estimation technique that can be considered a smoothed version of the Parzen windows presented in the Chapter 2. First, the most popular kernel types are presented together with a number of basic definitions both for uni- and multivariate cases and then a review of performance criteria is provided, starting ...
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Complementary Kernel Density Estimation

Pattern Recognition Letters, 2012
Generative models for vision and pattern recognition have been overshadowed in recent years by powerful non-parametric discriminative models. These discriminative models can learn arbitrary decision boundaries between classes and have proved very effective in classification and detection problems.
Xu Miao, Ali Rahimi, Rajesh P.N. Rao
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Kernel Density Estimation

1991
The idea of kernel estimators was introduced by Rosenblatt (1956). In Chapter 1 needles were used in the observations as a very noisy method to approximate density.
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Kernel density estimates for sepsis classification

Computer Methods and Programs in Biomedicine, 2020
Severe sepsis is a leading cause of intensive care unit (ICU) admission, length of stay, mortality, and cost. systemic inflammatory response syndrome (SIRS) and organ failure due to infection define it, but also make it hard to diagnose. Early diagnosis reduces morbidity, mortality and cost, and diagnosis is often significantly delayed due to a lack of
Parente, Jacquelyn D., Chase, James G., Möller, Knut, Shaw, Geoffrey M.   +3 more
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Reweighted kernel density estimation

Computational Statistics & Data Analysis, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hazelton, Martin L., Turlach, Berwin A.
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Filtered kernel density estimation

WIREs Computational Statistics, 2009
AbstractThis article describes a multiple‐bandwidth version of the kernel estimator for nonparametric probability density estimation, in which the bandwidths are chosen using a set of functions, called filter functions, which determine the support of the density appropriate to the different bandwidths. These filter functions are usually defined using a
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