Results 11 to 20 of about 200,311 (278)
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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Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm [PDF]
E3S Web of Conferences, 2023 The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms
Kumar H. Kishore, Kumar S. Ashok
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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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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Soil moisture (SM) plays a critical role in various fields such as agriculture, hydrology, and land-atmosphere interactions. This study aims to evaluate the performance of the categorical boosting algorithm (CatBoost) in comparison to other multiple ...
Ya Gao +4 more
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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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AddGBoost: A gradient boosting-style algorithm based on strong learners
Machine Learning with Applications, 2022 We present AddGBoost, a gradient boosting-style algorithm, wherein the decision tree is replaced by a succession of (possibly) stronger learners, which are optimized via a state-of-the-art hyperparameter optimizer.
Moshe Sipper, Jason H. Moore
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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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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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