Results 71 to 80 of about 3,667,438 (313)
Ridge Regression and PLS Regression
American Review of Mathematics and Statistics, 2023 A brief review of Ridge Regression (RR) and PLS Regression (PLS) is presented. Process and Spectral data are used in the analysis. Both are low-rank data, which is common in chemometric work. The Ridge constant k is determined by minimizing the size of the residuals in Leave-one-out RR.
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Single‐ and Dual‐Atom Configurations in Atomically Dispersed Catalysts for Lithium–Sulfur Batteries
Advanced Functional Materials, EarlyView.Single‐atom and dual‐atom‐based atomically dispersed catalysts (ADCs) effectively address the shuttle effect and sluggish redox kinetics in Li–S batteries. With nearly 100% atomic utilization and tunable coordination environments, ADCs enhance LiPSs adsorption, lower conversion barriers, and accelerate sulfur redox reactions.
Haoyang Xu +4 more
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Scalable Algorithms for the Sparse Ridge Regression [PDF]
SIAM Journal on Optimization, 2020 31 ...
Weijun Xie 0001, Xinwei Deng
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Advanced Functional Materials, EarlyView.Flexible piezoresistive pressure sensors underpin wearable and soft electronics. This review links sensing physics, including contact resistance modulation, quantum tunneling and percolation, to unified materials/structure design. We highlight composite and graded architectures, interfacial/porous engineering, and microstructured 3D conductive networks
Feng Luo +2 more
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Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
, 2011 Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker‐assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model.
Jeffrey B. Endelman
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Benign overfitting in ridge regression
J. Mach. Learn. Res., 2020 In many modern applications of deep learning the neural network has many more parameters than the data points used for its training. Motivated by those practices, a large body of recent theoretical research has been devoted to studying overparameterized models. One of the central phenomena in this regime is the ability of the model to interpolate noisy
Alexander Tsigler, Peter L. Bartlett
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Ridge Regression, Hubness, and Zero-Shot Learning
, 2015 This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space.
Hara, Kazuo +4 more
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Applications of Some Improved Estimators in Linear Regression [PDF]
, 2005 The problem of estimation of the regression coefficients under multicollinearity situation for the restricted linear model is discussed. Some improve estimators are considered, including the unrestricted ridge regression estimator (URRE), restricted ...
Kibria, B. M. Golam
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Advanced Healthcare Materials, EarlyView.A versatile approach is presented for fabricating smart multifunctional textiles by integrating thermo‐fluorescent carbon dot/polymer nanocomposite coatings with 3D‐printed interlocked architectures. The fabrics exhibit temperature‐responsive fluorescence, durable hydrophobicity, strong antibacterial and antioxidant activity, and enhanced UV protection.
Poushali Das +8 more
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Scientific African, 2022 The parameters in the Poisson regression model are usually estimated using the maximum likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or outliers in the Poisson regression model.
Kingsley C Arum +2 more
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