Results 21 to 30 of about 106,398 (294)
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
doaj +1 more source
Gradient boosting to boost the efficiency of hydraulic fracturing [PDF]
Journal of Petroleum Exploration and Production Technology, 2019 In this paper, we present a data-driven model for forecasting the production increase after hydraulic fracturing (HF). We use data from fracturing jobs performed at one of the Siberian oilfields. The data includes features, characterizing the jobs, and geological information.
Ivan Makhotin +2 more
openaire +3 more sources
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
doaj +1 more source
Distributional Gradient Boosting Machines
CoRR, 2022 Distributional Regression, LightGBM, Normalizing Flow, Probabilistic Forecasting ...
Alexander März, Thomas Kneib
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
openaire +3 more sources
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
doaj +1 more source
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
openaire +3 more sources
CoRR, 2015 We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online ...
Alina Beygelzimer +3 more
openaire +3 more sources