variable selection

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1
Non-destructive determination of strawberry fruit and juice quality parameters using ultraviolet, visible, and near-infrared spectroscopy.
Włodarska, Katarzyna;Szulc, Julia;Khmelinskii, Igor;Sikorska, Ewa;
2019 Journal of the science of food and agriculture DOI: 10.1002/jsfa.9870 294 views
2
Variable selection in covariate dependent random partition models: an application to urinary tract infection.
Barcella, William;Iorio, Maria De;Baio, Gianluca;Malone-Lee, James;
2016 Statistics in Medicine DOI: 10.1002/sim.6786 272 views
3
Image-Derived Traits Related to Mid-Season Growth Performance of Maize Under Nitrogen and Water Stress.
Dodig, Dejan;Božinović, Sofija;Nikolić, Ana;Zorić, Miroslav;Vančetović, Jelena;Ignjatović-Micić, Dra...
2019 Frontiers in plant science DOI: 10.3389/fpls.2019.00... 211 views
4
mapping tree canopy cover and aboveground biomass in sudano-sahelian woodlands using landsat 8 and random forest
;Martin Karlson;Madelene Ostwald;Heather Reese;Josias Sanou;Boalidioa Tankoano;Eskil Mattsson
2015 Journal of pharmacological sciences DOI: 10.3390/rs70810017 213 views
5
A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.
Claros-Olivares, Claudio C; Anderson, Melissa N; Qian, Wei; Brockmeier, Austin J; Buckley, Thomas A
2025 2019 18th international symposium infoteh-jahorina... DOI: 10.1101/2025.01.29.2... 17 views

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total phenolic content pls regression uv, vis, and nir spectroscopy soluble solids content clustering bayesian nonparametrics dirichlet process spike and slab priors nitrogen deficiency water stress high-throughput phenotyping maize genotypes stress indices vegetative biomass random forest landsat 8 woodland multi-temporal imagery Logistic regression concussion