Results 221 to 230 of about 1,593,883 (356)
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
Inference via Kernel Smoothing of Bootstrap P Values
Jeffrey S. Racine +3 more
openalex +2 more sources
Kernel Smoothing in Semiparametric Regression
A semiparametric regression model consists of parametric explanatory part of the response as well as nonparametric regression function of one or more variable(s) interpreting the response. The basic semiparametric regression model involves a linear function of a single parametric covariate as well as an unknown but preferably nonlinear function of a ...
openaire +1 more source
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Nonlinear kernel-based fMRI activation detection. [PDF]
Han C, Yang Z, Zhuang X, Cordes D.
europepmc +1 more source
On the Existence of Double Singular Integrals for Kernels without Smoothness [PDF]
T. Walsh
openalex +1 more source
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen +2 more
wiley +1 more source
Research on precise estimation of line loss rate probability density based on bilateral total variation filtering algorithm. [PDF]
Zhang J, Liu S, Feng C.
europepmc +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Accounting for Exposure Measurement Error in Gridded Air Pollution Estimates in Assessing the Association of PM<sub>2.5</sub> Exposures with Health Outcomes in Cohort Studies. [PDF]
Park ES +8 more
europepmc +1 more source

