Results 91 to 100 of about 5,004,642 (164)

Maternal essential and toxic metal mixtures in late pregnancy and neonatal birth weight

Environment International
Background: Prenatal exposure to toxic metals has been consistently associated with impaired fetal growth, yet pregnant individuals are simultaneously exposed to mixtures of essential and toxic metals.
Jiayi Xiang, Xiaoli Zhang, Katrin Huesker, Yvonne Liu, Johann-Georg Hocher, Xin Chen, Zeyu Zhang, Huifang Zhang, Bernhard K. Krämer, Volker von Baehr, Berthold Hocher   +10 more
doaj   +1 more source

Beyond multi-class – structured learning for machine translation

, 2010
In this thesis, we explore and present machine learning (ML) approaches to a particularly challenging research area – machine translation (MT). The study aims at replacing or developing each component in the MT system with an appropriate discriminative ...
Ni, Y, Ni, Yizhao
core   +1 more source

Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models

Scientific Reports
This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment.
Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah   +2 more
doaj   +1 more source

Cord blood metal(loid) concentrations and bile acid profiles: Insights from mixture analysis

Ecotoxicology and Environmental Safety
Bile acids (BAs) are essential signaling molecules that regulate metabolic and inflammatory processes. However, epidemiological evidence on the influence of metal(loid) mixtures on BA profiles remains scarce, particularly among newborns.
Chuyi Ou, Dan Luo, Lihong Wu, Bibai Du, Di Liu, Jun Yang, Weixiang Wu   +6 more
doaj   +1 more source

Individual and joint associations of PM _2.5 components with the mumps incidence: based on quantile g-computation and Bayesian kernel machine regression

International Journal of Environmental Health Research
Total PM2.5 mass has been shown to be associated with mumps. However, the individual and joint associations of PM2.5 components with mumps and the importance of each PM2.5 component remain unclear. To our knowledge, this ecological study is the first to investigate the associations of PM2.5 components with mumps.
Rui, Li, Xiaoshu, Zhang, Donghua, Li, Tingrong, Wang, Xiong, Yue, Tianshan, Shi, Zhenjuan, Li, Zixuan, Zou, Xiaowei, Ren   +8 more
openaire   +2 more sources

A sparse kernel density estimation algorithm using forward constrained regression

, 2007
Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection.
Hong, X., Harris, C., Harris, C.J., Chen, S.   +3 more
core  

Sparse Incremental Regression Modeling Using Correlation Criterion with Boosting Search

, 2005
A novel technique is presented to construct sparse generalized Gaussian kernel regression models. The proposed method appends regressors in an incremental modeling by tuning the mean vector and diagonal covariance matrix of individual Gaussian regressor ...
Wang, X.X., S. Chen, D. J. Brown, X. X. Wang, Chen, S., Brown, D.J.   +5 more
core   +1 more source

On robustness properties of convex risk minimization methods for pattern recognition [PDF]

The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness properties of machine learning methods based on convex risk minimization are investigated for the problem of pattern recognition ...
Christmann, Andreas, Steinwart, Ingo
core  

A Bayesian approach to parameter estimation for kernel density estimation via transformations [PDF]

In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations.
David Pitt, Xueyuan Wu, Qing Liu, Xibin Zhang   +3 more
core  

Probabilistic Shear Strength Model of RC Columns Based on Gaussian Process Regression with Anisotropic Compound Kernel Function

工程科学与技术
A probabilistic shear strength model for reinforced concrete (RC) columns is proposed based on Gaussian process regression (GPR) with an anisotropic mixed kernel function to address the limitations of traditional models, which often exhibit low nonlinear
Qiming LI, Pengfei ZHANG, Zecheng YU, Bo YU   +3 more
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