Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations
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- Wang, Hui & Rose, Sherri & van der Laan, Mark J., 2011. "Finding quantitative trait loci genes with collaborative targeted maximum likelihood learning," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 792-796, July.
- Vaart Aad W. van der & Dudoit Sandrine & Laan Mark J. van der, 2006. "Oracle inequalities for multi-fold cross validation," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 351-371, December.
- Sinisi Sandra E. & Polley Eric C & Petersen Maya L & Rhee Soo-Yon & van der Laan Mark J., 2007. "Super Learning: An Application to the Prediction of HIV-1 Drug Resistance," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-26, February.
- Elizabeth M Sweeney & Joshua T Vogelstein & Jennifer L Cuzzocreo & Peter A Calabresi & Daniel S Reich & Ciprian M Crainiceanu & Russell T Shinohara, 2014. "A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
- Cheng Ju & Mary Combs & Samuel D. Lendle & Jessica M. Franklin & Richard Wyss & Sebastian Schneeweiss & Mark J. van der Laan, 2019. "Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(12), pages 2216-2236, September.
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- Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.
- Dicho S. Stratiev & Svetoslav Nenov & Ivelina K. Shishkova & Rosen K. Dinkov & Kamen Zlatanov & Dobromir Yordanov & Sotir Sotirov & Evdokia Sotirova & Vassia Atanassova & Krassimir Atanassov & Danail , 2021. "Comparison of Empirical Models to Predict Viscosity of Secondary Vacuum Gas Oils," Resources, MDPI, vol. 10(8), pages 1-17, August.
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Keywords
viscosity; PVT properties; dead oil viscosity; machine learning; SuperLearner;All these keywords.
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