Results 31 to 40 of about 914,879 (290)
Forecasting with many predictors - Is boosting a viable alternative? [PDF]
, 2010 This paper evaluates the forecast performance of boosting, a variable selection device, and compares it with the forecast combination schemes and dynamic factor models presented in Stock and Watson (2006).
Wohlrabe, Klaus, Buchen, Teresa
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Gradient boosting for quantitative finance
The Journal of Computational Finance, 2021 In this paper, we discuss how tree-based machine learning techniques can be used in the context of derivatives pricing. Gradient boosted regression trees are employed to learn the pricing map for a couple of classical, time-consuming problems in quantitative finance.
Davis, Jesse +3 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.
Griesbach, Colin +2 more
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Remote Sensing, 2023 This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the boundaries and edges of land cover classes present in the dataset.
Achala Shakya +2 more
doaj +1 more source
Accelerating Gradient Boosting Machine
CoRR, 2019 Gradient Boosting Machine (GBM) is an extremely powerful supervised learning algorithm that is widely used in practice. GBM routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In this work, we propose Accelerated Gradient Boosting Machine (AGBM) by incorporating Nesterov's acceleration techniques ...
Lu, Haihao +3 more
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Flexible Boosting of Accelerated Failure Time Models [PDF]
, 2008 When boosting algorithms are used for building survival models from high-dimensional data, it is common to fit a Cox proportional hazards model or to apply semiparametric least squares techniques.
Torsten Hothorn +5 more
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Machine learning techniques for classifying dangerous asteroids
MethodsX, 2023 There is an infinite number of objects in outer space, and these objects and asteroids might be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those.Therefore, in this article, with the hyperparameters tuning of ...
Seyed Matin Malakouti +2 more
doaj +1 more source
Gradient boosting decision tree algorithm.
, 2021 Gradient boosting decision tree algorithm.
Wenjing Xu (183446) +5 more
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Boosting Additive Models using Component-wise P-Splines [PDF]
, 2007 We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous ...
Hothorn, Torsten, Schmid, Matthias
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Applied Sciences, 2022 Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms
Antonio Panarese +3 more
doaj +1 more source