LAUSR

Discriminative ridge regression algorithm for adaptation in statistical machine translation

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0 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

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1 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

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0 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

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0 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

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0 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

Bayes minimax ridge regression estimators

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0 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

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0 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

An Accelerated Maximally Split ADMM for a Class of Generalized Ridge Regression.

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1 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

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1 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

Ridge Regression Method and Bayesian Estimators under Composite LINEX Loss Function to Estimate the Shape Parameter in Lomax Distribution

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1 Published in 2022 at "Computational Intelligence and Neuroscience"

DOI: 10.1155/2022/1200611

Abstract: In this paper, the Ridge Regression method is employed to estimate the shape parameter of the Lomax distribution (LD). In addition to that, the approaches of both classical and Bayesian are considered with several loss… read more here.

Keywords: ridge regression; shape parameter; regression method; method ... See more keywords

Ridge regression for the functional concurrent model

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1 Published in 2018 at "Electronic Journal of Statistics"

DOI: 10.1214/18-ejs1412

Abstract: The aim of this paper is to propose estimators of the unknown functional coefficients in the Functional Concurrent Model (FCM). We extend the Ridge Regression method developed in the classical linear case to the functional… read more here.

Keywords: ridge regression; regression functional; functional concurrent; concurrent model ... See more keywords