Results 141 to 150 of about 3,667,438 (313)
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
Advanced Intelligent Discovery, EarlyView.This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley +1 more source
Ridge Regression Model Averaging Based on Rp Criterion
Journal of MathematicsModel selection is an important tool in statistical inference. However, it is useless with regard to the uncertainty inherent in the inference process.
Yunfei Guo, Tao Wang, Zhonghua Li
doaj +1 more source
The Challenge of Handling Structured Missingness in Integrated Data Sources
Advanced Intelligent Discovery, EarlyView.As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
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
OKRidge: Scalable Optimal k-Sparse Ridge Regression. [PDF]
Adv Neural Inf Process Syst, 2023 Liu J, Rosen S, Zhong C, Rudin C.
europepmc +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
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
The Distribution of Stochastic Shrinkage Parameters in Ridge Regression [PDF]
In this article we derive the density and distribution functions of the stochastic shrinkage parameters of three well-known operational Ridge Regression estimators by assuming normality. The stochastic behavior of these parameters is likely to affect the
Hernán Rubio, Luis Firinguetti
core
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