Discriminative ridge regression algorithm for adaptation in statistical machine translation
Sign Up to like & getrecommendations! Published in 2018 at "Pattern Analysis and Applications"
DOI: 10.1007/s10044-018-0720-5
Abstract: We present a simple and reliable method for estimating the log-linear weights of a state-of-the-art machine translation system, which takes advantage of the method known as discriminative ridge regression (DRR). Since inappropriate weight estimations lead… read more here.
Keywords: ridge regression; translation; machine translation; discriminative ridge ... See more keywords
Robust object tracking based on ridge regression and multi-scale local sparse coding
Sign Up to like & getrecommendations! Published in 2019 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-019-08139-2
Abstract: Recently, the technology of visual object tracking has achieved great success. However, it is still extraordinary challenging for some factors, such as scale variations, partial occlusions and so on. To deal with the problem of… read more here.
Keywords: regression; scale variations; ridge regression; based ridge ... See more keywords
Evaluation of ridge regression for country-wide prediction of genotype-specific grain yields of wheat
Sign Up to like & getrecommendations! Published in 2018 at "Agricultural and Forest Meteorology"
DOI: 10.1016/j.agrformet.2017.12.263
Abstract: Abstract Large scale prediction of the performance of genotypes is fundamental for understanding genotype by environmental interactions (G × E), predicting accurately genotypic performance in specific environments, and increasing our knowledge to develop future crop varieties. We… read more here.
Keywords: wheat; ridge regression; genotype; limiting factors ... See more keywords
Robust ridge regression based on self-paced learning for multivariate calibration
Sign Up to like & getrecommendations! Published in 2018 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2018.03.004
Abstract: Abstract In this paper, we propose a robust ridge regression model based on self-paced learning (RR-SPL) for the high-dimensional spectroscopic data. The proposed RR-SPL model consists of a weighted least-squares loss term on all training… read more here.
Keywords: model; self paced; ridge regression;
Diagnostic measures for kernel ridge regression on reproducing kernel Hilbert space
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the Korean Statistical Society"
DOI: 10.1016/j.jkss.2019.01.003
Abstract: Abstract The aim of this paper is to define and develop diagnostic measures with respect to kernel ridge regression in a reproducing kernel Hilbert space (RKHS). To identify influential observations, we define a particular version… read more here.
Keywords: regression; regression reproducing; reproducing kernel; diagnostic measures ... See more keywords
Weighted penalized m-estimators in robust ridge regression: an application to gasoline consumption data
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2024.2386391
Abstract: ABSTRACT The OLS and ridge regression (RR) estimators are adversely affected, when the problem of multicollinearity and y-direction outliers occur together. The robust ridge regression with penalized parameters offers biased estimators with lower variance than… read more here.
Keywords: gasoline consumption; penalized estimators; robust ridge; ridge regression ... See more keywords
Bayes minimax ridge regression estimators
Sign Up to like & getrecommendations! Published in 2018 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2017.1397167
Abstract: ABSTRACT The problem of estimating of the vector β of the linear regression model y = Aβ + ϵ with ϵ ∼ Np(0, σ2Ip) under quadratic loss function is considered when common variance σ2 is… read more here.
Keywords: regression estimators; minimax estimators; regression; ridge regression ... See more keywords
Quantum Algorithm for Spectral Regression for Regularized Subspace Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2018.2886581
Abstract: In this paper, we propose an efficient quantum algorithm for spectral regression which is a dimensionality reduction framework based on the regression and spectral graph analysis. The quantum algorithm involves two core subroutines: the quantum… read more here.
Keywords: regression; algorithm spectral; quantum algorithm; spectral regression ... See more keywords
Privacy-Preserving Ridge Regression Over Encrypted Data Under Multiple Keys
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2025.3554563
Abstract: With the increase of private data being collected by data owners, it has been a trend for data owners to store the data on cloud computing platforms. The huge amounts of data in cloud servers… read more here.
Keywords: privacy preserving; privacy; preserving ridge; machine learning ... See more keywords
An Accelerated Maximally Split ADMM for a Class of Generalized Ridge Regression.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3104840
Abstract: Ridge regression (RR) has been commonly used in machine learning, but is facing computational challenges in big data applications. To meet the challenges, this article develops a highly parallel new algorithm, i.e., an accelerated maximally… read more here.
Keywords: regression; new algorithm; admm class; accelerated maximally ... See more keywords
Risk Convergence of Centered Kernel Ridge Regression With Large Dimensional Data
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2020.2975939
Abstract: This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call centered kernel ridge regression (CKRR), also known in the literature as kernel ridge regression with offset. This… read more here.
Keywords: regression; risk; large dimensional; kernel ridge ... See more keywords