Results 21 to 30 of about 914,879 (290)
G3: Genes, Genomes, Genetics, 2022 We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP
Bruno C Perez +4 more
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Distributional Gradient Boosting Machines
CoRR, 2022 Distributional Regression, LightGBM, Normalizing Flow, Probabilistic Forecasting ...
Alexander März, Thomas Kneib
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Case Studies in Chemical and Environmental Engineering, 2023 This research used machine-learning-based forecasting models to estimate a Supervisory control and data acquisition system's wind speed and electricity production.
Seyed Matin Malakouti
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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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Federated Functional Gradient Boosting
CoRR, 2021 In this paper, we initiate a study of functional minimization in Federated Learning. First, in the semi-heterogeneous setting, when the marginal distributions of the feature vectors on client machines are identical, we develop the federated functional gradient boosting (FFGB) method that provably converges to the global minimum. Subsequently, we extend
Zebang Shen +3 more
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Boosting the Anatomy of Volatility [PDF]
, 2012 Risk and, thus, the volatility of financial asset prices plays a major role in financial decision making and financial regulation. Therefore, understanding and predicting the volatility of financial instruments, asset classes or financial markets in ...
Mittnik, Stefan +2 more
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A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
IEEE Access, 2022 Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models.
Ibomoiye Domor Mienye, Yanxia Sun
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Individually Fair Gradient Boosting
CoRR, 2021 We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our ...
Alexander Vargo +3 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.
Xiao Liu 0044, Rong Pan
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