Kennard-Stone method outperforms the Random Sampling in the selection of calibration samples in SNPs and NIR data

Autor: Roberta de Amorim Ferreira, Gabriely Teixeira, Luiz Alexandre Peternelli

Publikováno v: Ciência Rural, Vol 52, Iss 5 (2021)

ABSTRACT: Splitting the whole dataset into training and testing subsets is a crucial part of optimizing models. This study evaluated the influence of the choice of the training subset in the construction of predictive models, as well as on their vali

Externí odkaz: https://doaj.org/article/fb3899b0c0444213993fe4653b55f3db

Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits

Autor: Patricia Mendes dos Santos, Ana Carolina Campana Nascimento, Moysés Nascimento, Fabyano Fonseca e Silva, Camila Ferreira Azevedo, Rodrigo Reis Mota, Simone Eliza Facioni Guimarães, Paulo Sávio Lopes

Publikováno v: Pesquisa Agropecuária Brasileira, Vol 53, Iss 9, Pp 1011-1017 (2018)

Abstract: The objective of this work was to evaluate the use of regularized quantile regression (RQR) to predict the genetic merit of pigs for asymmetric carcass traits, compared with the Bayesian lasso (Blasso) method. The genetic data of the traits

Externí odkaz: https://doaj.org/article/af6b9cbcdbc34ba293c9bca91f24a5e5

The Geometry of Off-the-Grid Compressed Sensing

Autor: Gabriel Peyré, Nicolas Keriven, Clarice Poon

Publikováno v: Foundations of Computational Mathematics
Foundations of Computational Mathematics, 2023, 23, pp.241-327. ⟨10.1007/s10208-021-09545-5⟩
Poon, C, Keriven, N & Peyré, G 2023, ' The geometry of off-the-grid compressed sensing ', Foundations of Computational Mathematics, vol. 23, pp. 241-327 . https://doi.org/10.1007/s10208-021-09545-5
Foundations of Computational Mathematics, Springer Verlag, In press, ⟨10.1007/s10208-021-09545-5⟩
Foundations of Computational Mathematics, Springer Verlag, 2021

Compressed sensing (CS) ensures the recovery of sparse vectors from a number of randomized measurements proportional to their sparsity. The initial theory considers discretized domains, and the randomness makes the physical positions of the grid node

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eef61b2c1b003d0677a2adeb19042340
https://doi.org/10.1007/s10208-021-09545-5

Methodological Comparison in Genomic-Wide Association Studies for Several Traits in Arabidopsis and Soybean

Autor: Medrine Mmayi Odinga, Feng, Jian-Ying, Ni, Yuan-Li, Yue, Xiu-Li, Zhang, Yuan-Ming

In genome-wide association studies (GWAS), hundreds of thousands single-nucleotide polymorphisms (SNPs) are genotyped for several hundreds of individuals. Usually, only a very smaller subset of these markers is associated with the trait. Existing met

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6911629123d1f409d8204feafaee95a0

Sparse Optimization on Measures with Over-parameterized Gradient Descent

Autor: Lénaïc Chizat

Minimizing a convex function of a measure with a sparsity-inducing penalty is a typical problem arising, e.g., in sparse spikes deconvolution or two-layer neural networks training. We show that this problem can be solved by discretizing the measure a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b7a0184ea9ea723f00465325463146c
https://hal.archives-ouvertes.fr/hal-02190822