Results 111 to 120 of about 3,187,155 (299)
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
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
Screening Cut Generation for Sparse Ridge Regression
Sparse ridge regression is widely utilized in modern data analysis and machine learning. However, computing globally optimal solutions for sparse ridge regression is challenging, particularly when samples are arbitrarily given or generated under weak modeling assumptions. This paper proposes a novel cut-generation method, Screening Cut Generation (SCG),
Tan, Haozhe, Wang, Guanyi
openaire +2 more sources
Ridge regression revisited [PDF]
We argue in this paper that general ridge (GR) regression implies no major complication compared with simple ridge regression. We introduce a generalization of an explicit GR estimator derived by Hemmerle and by Teekens and de Boer and show that this ...
Hafner, C.M., Boer, P.M.C. de
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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 +4 more
wiley +1 more source
Data Science in ScienceRidge, Liu, and Kibria–Lukman regression methods that have been proposed to solve the multicollinearity problem for both linear and generalized linear regression models (Kibria and Lukman, Shewa and Ugwuowo). This paper considers several different ridge,
Sergio Perez Melo +3 more
doaj +1 more source
Generalized Ridge Regression Estimator in High Dimensional Sparse Regression Models
Statistics, Optimization & Information Computing, 2018 Modern statistical analysis often encounters linear models with the number of explanatory variables much larger than the sample size. Estimation in these high-dimensional problems needs some regularization methods to be employed due to rank deficiency of the design matrix.
openaire +2 more sources
"Minimax Empirical Bayes Ridge-Principal Component Regression Estimators" [PDF]
In this paper, we consider the problem of estimating the regression parameters in a multiple linear regression model with design matrix A when the multicollinearity is present.
Tatsuya Kubokawa, M. S. Srivastava
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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 Learning Algorithm in Dual Variables
, 1998 In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space.
C. Saunders +5 more
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