Results 11 to 20 of about 66,574 (258)
Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications. [PDF]
PLoS ONE, 2016 OBJECTIVE:To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. DESIGN:Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010.
Paul Thottakkara +6 more
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Land-Subsidence Spatial Modeling Using Generalized Additive Model Data Mining Technique [PDF]
پژوهشهای آبخیزداری, 2017 Land-subsidence phenomenon is one of the geomorphologic hazards known in arid and semi-arid areas in recent years. The main objective of the present study is to provide a land-subsidence spatial modeling and its assessment using the generalized additive ...
Hamid Reza Pourghasemi +1 more
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Mapping spatial patterns of plant species based on machine-learning and regression models [PDF]
Desert, 2022 Various statistical techniques have been used for species distribution modeling that attempt to predict the occurrence of a given species with respect to environmental conditions.
H. Keshtkar, P. Pourmohammad
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Distribution Modeling of Protective and Valuable Plant Species in the Tourist Area of Polour Using Generalized Linear Model (GLM) and Generalized Additive Model(GAM) [PDF]
جغرافیا و توسعه, 2017 The prediction models of geographical distribution of the plant species are probabilistic and static models. They determinate the mathematical equations governing on geographical distribution of species with their current environment and environmental ...
Zeinab Jafarian, Mansoureh Kargar
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Revista de Saúde Pública, 2014 OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study.
Juliana Bottoni de Souza +3 more
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Interpretable Ranking with Generalized Additive Models [PDF]
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021 Interpretability of ranking models is a crucial yet relatively under-examined research area. Recent progress on this area largely focuses on generating post-hoc explanations for existing black-box ranking models. Though promising, such post-hoc methods cannot provide sufficiently accurate explanations in general, which makes them infeasible in many ...
Honglei Zhuang +9 more
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Generalized Sparse Additive Models
Journal of machine learning research : JMLR, 2019 We present a unified framework for estimation and analysis of generalized additive models in high dimensions. The framework defines a large class of penalized regression estimators, encompassing many existing methods. An efficient computational algorithm for this class is presented that easily scales to thousands of observations and features.
Asad Haris, Noah Simon, Ali Shojaie
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Journal of Dairy Science, 2022 : Reduction of milk yield is one of the principal components in the cost of mastitis. However, past research into the association between milk yield and mastitis indicators is limited.
John Bonestroo +5 more
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Quantile Generalized Additive Model a Robust Alternative to Generalized Additive Model
International Journal of Mathematical Research, 2021 Nonparametric regression is an approach used when the structure of the relationship between the response and the predictor variable is unknown. It tries to estimate the structure of this relationship since there is no predetermined form. The generalized additive model (GAM) and quantile generalized additive (QGAM) model provides an attractive framework
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Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv
Journal of Statistical Software, 2016 The BUGS language offers a very flexible way of specifying complex statistical models for the purposes of Gibbs sampling, while its JAGS variant offers very convenient R integration via the rjags package.
Simon N. Wood
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