Results 21 to 30 of about 197,316 (278)
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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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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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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Boosting insights in insurance tariff plans with tree-based machine learning methods [PDF]
, 2020 Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of ...
Antonio, Katrien +3 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
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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
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Causal Gradient Boosting: Boosted Instrumental Variable Regression
Proceedings of the 23rd ACM Conference on Economics and Computation, 2022 Recent advances in the literature have demonstrated that standard supervised learning algorithms are ill-suited for problems with endogenous explanatory variables. To correct for the endogeneity bias, many variants of nonparameteric instrumental variable regression methods have been developed.
Bakhitov, Edvard, Singh, Amandeep
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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
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Identifying Parkinson’s Patients: A Functional Gradient Boosting Approach [PDF]
, 2017 Parkinson’s, a progressive neural disorder, is difficult to identify due to the hidden nature of the symptoms associated. We present a machine learning approach that uses a definite set of features obtained from the Parkinson’s Progression Markers ...
Dhami, D. S. +3 more
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Geoadditive Regression Modeling of Stream Biological Condition [PDF]
, 2010 Indices of biotic integrity (IBI) have become an established tool to quantify the condition of small non-tidal streams and their watersheds. To investigate the effects of watershed characteristics on stream biological condition, we present a new ...
Hothorn, Torsten +4 more
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