Results 41 to 50 of about 535,598 (86)
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R [PDF]
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
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Annular and circular rigid inclusions planted into a penny-shaped crack and factorization of triangular matrices. [PDF]
Proc Math Phys Eng Sci, 2020 Antipov YA, Mkhitaryan SM.
europepmc +1 more source
Nonlinear Knowledge in Kernel Approximation [PDF]
, 2006 Prior knowledge over arbitrary general sets is incorporated into nonlinear kernel approximation problems in the form of linear constraints in a linear program.
Mangasarian, Olvi, Wild, Edward
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Sparse kernel modelling: a unified approach
, 2007 A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria
Hong, X., Harris, C.J., Chen, S.
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Parsimonious Kernel Fisher Discrimination
, 2007 By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution.
Harrison, Robert F. +5 more
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kernlab - An S4 Package for Kernel Methods in R [PDF]
kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels),
Kurt Hornik +3 more
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Systematic investigation of theories of transport in the Lennard-Jones fluid. [PDF]
J Chem Phys, 2007 Dyer KM, Pettitt BM, Stell G.
europepmc +1 more source
Maximum kernel likelihood estimation [PDF]
, 2008 We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational
Jaki, Thomas, West, R. Webster
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Nonparametric Beta kernel estimator for long memory time series [PDF]
The paper introduces a new nonparametric estimator of the spectral density that is given in smoothing the periodogram by the probability density of Beta random variable (Beta kernel).
VAN BELLEGEM, Sébastien +1 more
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Weighted Sonine conditions and application
The Sonine kernel described by the classical Sonine condition of convolution form is an important class of kernels used in integral equations and nonlocal differential equations.
Zhu, Shangqin +2 more
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