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Optimization by gradient boosting [PDF]
, 2017 Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees---by solving an infinite-dimensional convex optimization problem.
Biau, Gérard, Cadre, Benoît
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Gradient Boosting Machines, A Tutorial [PDF]
Frontiers in Neurorobotics, 2013 Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications.
Alexey eNatekin, Alois eKnoll
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Modeling CO2 solubility in water using gradient boosting and light gradient boosting machine
Scientific ReportsThe growing application of carbon dioxide (CO2) in various environmental and energy fields, including carbon capture and storage (CCS) and several CO2-based enhanced oil recovery (EOR) techniques, highlights the importance of studying the phase ...
Atena Mahmoudzadeh +7 more
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Randomized Gradient Boosting Machine [PDF]
SIAM Journal on Optimization, 2020 Gradient Boosting Machine (GBM) introduced by Friedman is a powerful supervised learning algorithm that is very widely used in practice---it routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In spite of the usefulness of GBM in practice, our current theoretical understanding of this method is ...
Lu, Haihao, Mazumder, Rahul
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Accelerated gradient boosting [PDF]
Machine Learning, 2019 Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov's accelerated descent to design a new algorithm, which we call AGB (for Accelerated Gradient Boosting).
Biau, Gérard +2 more
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Boost-R: Gradient boosted trees for recurrence data [PDF]
Journal of Quality Technology, 2021 Recurrence data arise from multi-disciplinary domains spanning reliability, cyber security, healthcare, online retailing, etc. This paper investigates an additive-tree-based approach, known as Boost-R (Boosting for Recurrence Data), for recurrent event data with both static and dynamic features.
Liu, Xiao, Pan, Rong
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Gradient boosting for linear mixed models [PDF]
The International Journal of Biostatistics, 2021 Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction ...
Griesbach, Colin +2 more
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Gradient boosted feature selection [PDF]
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014 A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure.
Xu, Zhixiang Eddie +3 more
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A meta-learning based stacked regression approach for customer lifetime value prediction
Journal of Economy and Technology, 2023 Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue, and this could be achieved only by understanding the customers more.
Karan Gadgil +2 more
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Using machine learning to improve risk prediction in durable left ventricular assist devices.
PLoS ONE, 2021 Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist ...
Arman Kilic +4 more
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