Results 131 to 140 of about 178,006 (285)

Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression. [PDF]

Sensors (Basel), 2023
Laufer B, Docherty PD, Murray R, Krueger-Ziolek S, Jalal NA, Hoeflinger F, Rupitsch SJ, Reindl L, Moeller K.   +8 more
europepmc   +1 more source

"Improved Empirical Bayes Ridge Regression Estimators under Multicollinearity" [PDF]

In this paper we consider the problem of estimating the regression parameters in a multiple linear regression model when the multicollinearity is present.Under the assumption of normality, we present three empirical Bayes estimators.
M. S. Srivastava, Tatsuya Kubokawa
core  

The NFDA-Nonsmooth Feasible Directions Algorithm applied to construction of Pareto Fronts of Ridge and Lasso Regressions

Trends in Computational and Applied Mathematics
Ridge and Lasso regressions are types of linear regression, a machine learning tool for dealing with data. Based on multiobjective optimization theory, we transform Ridge and Lasso regression into bi-objective optimization problems. The Pareto fronts of
W. P. Freire
doaj   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers. [PDF]

BMC Genom Data, 2023
Lozada DN, Sandhu KS, Bhatta M.
europepmc   +1 more source

Using ridge regression in systematic pointing error corrections [PDF]

A pointing error model is used in the antenna calibration process. Data from spacecraft or radio star observations are used to determine the parameters in the model.
Guiar, C. N.
core   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression. [PDF]

J Gerontol A Biol Sci Med Sci, 2023
Massett RJ, Maher AS, Imms PE, Amgalan A, Chaudhari NN, Chowdhury NF, Irimia A, Alzheimer’s Disease Neuroimaging Initiative.   +7 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Ridge Regression for Functional Form Identification of Continuous Predictors of Clinical Outcomes in Glomerular Disease. [PDF]

Glomerular Dis, 2023
Rubin J, Mariani L, Smith A, Zee J.
europepmc   +1 more source