Results 251 to 260 of about 115,501 (297)

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

Leaf beetle diversity on a Southeast Asian continental island: Taxonomy, DNA barcoding, and preliminary evolutionary insights from Cat Ba Island, Vietnam. [PDF]

PLoS One
Nguyen DT, Ho LT.
europepmc   +1 more source

Bayesian spatiotemporal modelling of neonatal, infant and under-5 mortality (2000-2022) in 41 Asian countries: a population-level observational study. [PDF]

BMJ Open
Rahman MS, Shiddik MAB.
europepmc   +1 more source

A Review of Source-Term Estimation for Continuous Methane Monitoring: From Data Acquisition to Modeling and Estimation. [PDF]

ACS Omega
Xie Z, Tang J, Li R, Tian G, Ma R.
europepmc   +1 more source

Latent profile analysis for kinesiophobia in patients undergoing coronary artery bypass grafting. [PDF]

Front Rehabil Sci
Luo Q, Guo X, Xu Y, Yang Y, Li M.
europepmc   +1 more source

Fear of progression subtypes and their influencing factors in patients with coronary heart disease: A cross-sectional study using latent profile analysis. [PDF]

Medicine (Baltimore)
Bai K, Huang W, Luo J, Deng Z, Liu S, Yang M, Tian J, Ma Y.   +7 more
europepmc   +1 more source

Bayesian model prediction of colorectal cancer incidence in Jiangsu Province, China, a cancer registry-based study. [PDF]

BMC Public Health
Luo P, Miao W, Wu X, Yu H, Qin Y, Zhou J, Han R.   +6 more
europepmc   +1 more source

Bayesian model selection and prediction with empirical applications: comment.

Palm, Franz C.
core  

Marker Selection by Akaike Information Criterion and Bayesian Information Criterion

Genetic Epidemiology, 2001
We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected‐affected versus affected‐unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters.
Wentian Li, Dale R Nyholt
exaly   +4 more sources