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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Comportamento estocástico do algoritmo kernel least-mean-square [PDF]
, 2012 Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Algoritmos baseados em kernel têm-se tornado populares no processamento não-linear de sinais.
Parreira, Wemerson Delcio
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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
core +1 more source
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
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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RDA Recommendation on PID Kernel Information (draft)
, 2018 Global middleware infrastructure is insufficient for robust data identification, discovery, and use. While infrastructure is emerging within sub-ecosystems such as the DOI ecosystem of services purposed for data and literature objects (i.e., DataCite ...
Schwardmann, Ulrich +6 more
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A Morphological Associative Memory Employing A Stored Pattern Independent Kernel Image and Its Hardware Model [PDF]
, 2009 An associative memory provides a convenient way for pattern retrieval and restoration, which has an important role for handling data distorted with noise.
Harada, Hidetaka +2 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
A Bayesian approach to parameter estimation for kernel density estimation via transformations [PDF]
In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations.
David Pitt +3 more
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