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Mostrando ítems 11-20 de 1880
Performance Prediction for Enhancing Ensemble Learning
(Universidade Federal de Minas GeraisUFMG, 2018-08-31)
Ensembling machine-learned models has shown to be a useful technique for improving the effectiveness of tasks such as classification, ad-hoc retrieval, and recommendation. Stacking, for instance, learns to weight and combine ...
Intraseasonal Ensemble Forecasting for the Brazilian Northeastern
(Universidade Federal de Santa Maria, 2019)
Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
(Ieee-inst Electrical Electronics Engineers IncPiscatawayEUA, 2011)
Assessment of South America summer rainfall climatology and trends in a set of Global Climate Models Large Ensembles
(John Wiley & Sons Ltd, 2020-05)
The purpose of this study is to assess the ability of a set of large ensembles (LE) of Global Climate Model (GCM) simulations from the Multi-Model Large Ensemble Archive, Coupled Model Intercomparison Project Phase 6 (CMIP6) ...
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
(Meteorological Soc Jpn, 2013-06)
In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two ...
The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
(Elsevier Science, 2015-09)
Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. ...
Electrical load prediction of healthcare buildings through single and ensemble learning
Healthcare buildings are characterized by complex energy systems and high energy usage, therefore
serving as the key areas for achieving energy conservation goals in the building sector. An accurate
load prediction of ...
Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux
(Elsevier, 2011-01)
Dynamic models of metabolism are instrumental for gaining insight and predicting possible outcomes of perturbations. Current approaches start from the selection of lumped enzyme kinetics and determine the parameters within ...
Convolutional neural networks ensembles through single-iteration optimization
(2022-01-01)
Convolutional Neural Networks have been widely employed in a diverse range of computer vision-based applications, including image classification, object recognition, and object segmentation. Nevertheless, one weakness of ...