Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods [PDF]
Genetics Research, 2010 SummaryPrediction of genetic values is a central problem in quantitative genetics. Over many decades, such predictions have been successfully accomplished using information on phenotypic records and family structure usually represented with a pedigree.
Gustavo de los Campos +4 more
openalex +3 more sources
Reproducing Kernel Hilbert Space Methods for wide-sense self-similar Processes [PDF]
The Annals of Applied Probability, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carl Nuzman, H. Vincent Poor
openalex +4 more sources
Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits [PDF]
Genetics, 2008 Abstract Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective.
Daniel Gianola, J.B.C.H.M. van Kaam
openalex +4 more sources
Reproducing kernel method for solving wiener-hopf equations of the second kind [PDF]
Journal of Hyperstructures, 2016 This paper proposed a reproducing kernel method for solving Wiener-Hopf equations of the second kind. In order to eliminate the singularity of the equation, a transform is used.
Azizallah Alvandi +2 more
doaj +1 more source
Mathematical Modelling and Analysis, 2021 The main aim of this paper is to present a hybrid scheme of both meshless Galerkin and reproducing kernel Hilbert space methods. The Galerkin meshless method is a powerful tool for solving a large class of multi-dimension problems.
Mehdi Mesrizadeh, Kamal Shanazari
doaj +1 more source
A method for approximate missing data from data error measured with l norm [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2021 We briefly review some recent work on hypercircle inequality for partially corrupted data when the data error is measured with l norm. The aim of this paper is to present the method for approximate missing data in the use of midpoint algorithm and
Benjawan Rodjanadid, Kannika Khompungson
doaj +1 more source
A novel method for fractal-fractional differential equations
Alexandria Engineering Journal, 2022 We consider the reproducing kernel Hilbert space method to construct numerical solutions for some basic fractional ordinary differential equations (FODEs) under fractal fractional derivative with the generalized Mittag–Leffler (M-L) kernel.
Nourhane Attia +4 more
doaj +1 more source
Learning Methods in Reproducing Kernel Hilbert Space Based on High-dimensional Features
, 2019 The first topic focuses on the dimension reduction method via the regularization. We propose the selection for principle components via LASSO. This method assumes that some unknown latent variables are related to the response under the highly correlate covariate structure.
Hojin Yang
openalex +3 more sources
A Novel Method for Solutions of Fourth-Order Fractional Boundary Value Problems
Fractal and Fractional, 2019 In this paper, we find the solutions of fourth order fractional boundary value problems by using the reproducing kernel Hilbert space method. Firstly, the reproducing kernel Hilbert space method is introduced and then the method is applied to this kind ...
Ali Akgül, Esra Karatas Akgül
doaj +1 more source
Solving Duffing-Van der Pol Oscillator Equations of Fractional Order by an Accurate Technique [PDF]
Journal of Applied and Computational Mechanics, 2021 In this paper, an accurate technique is used to find an approximate solution to the fractional-order Duffing-Van der Pol (DVP, for short) oscillators equation which is reproducing kernel Hilbert space (RKHS, for short ) method. The numerical results show
Nourhane Attia +3 more
doaj +1 more source