Matches in SemOpenAlex for { <https://semopenalex.org/work/W2087918196> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W2087918196 abstract "In this work we will analyze and apply to the prediction of wind energy some of the best known regularized linear regression algorithms, such as Ordinary Least Squares, Ridge Regression and, particularly, Lasso, Group Lasso and Elastic-Net that also seek to impose a certain degree of sparseness on the final models. To achieve this goal, some of them introduce a non-differentiable regularization term that requires special techniques to solve the corresponding optimization problem that will yield the final model. Proximal Algorithms have been recently introduced precisely to handle this kind of optimization problems, and so we will briefly review how to apply them in regularized linear regression. Moreover, the proximal method FISTA will be used when applying the non-differentiable models to the problem of predicting the global wind energy production in Spain, using as inputs numerical weather forecasts for the entire Iberian peninsula. Our results show how some of the studied sparsity-inducing models are able to produce a coherent selection of features, attaining similar performance to a baseline model using expert information, while making use of less data features." @default.
- W2087918196 created "2016-06-24" @default.
- W2087918196 creator A5007833886 @default.
- W2087918196 creator A5031174137 @default.
- W2087918196 creator A5046362615 @default.
- W2087918196 date "2012-06-01" @default.
- W2087918196 modified "2023-10-12" @default.
- W2087918196 title "Sparse methods for wind energy prediction" @default.
- W2087918196 cites W1501916037 @default.
- W2087918196 cites W1575567117 @default.
- W2087918196 cites W1886579186 @default.
- W2087918196 cites W2047028564 @default.
- W2087918196 cites W2063978378 @default.
- W2087918196 cites W2070094080 @default.
- W2087918196 cites W2100556411 @default.
- W2087918196 cites W2122825543 @default.
- W2087918196 cites W2135046866 @default.
- W2087918196 cites W2138019504 @default.
- W2087918196 cites W2140514146 @default.
- W2087918196 cites W2206218737 @default.
- W2087918196 cites W2951870246 @default.
- W2087918196 doi "https://doi.org/10.1109/ijcnn.2012.6252843" @default.
- W2087918196 hasPublicationYear "2012" @default.
- W2087918196 type Work @default.
- W2087918196 sameAs 2087918196 @default.
- W2087918196 citedByCount "3" @default.
- W2087918196 countsByYear W20879181962013 @default.
- W2087918196 countsByYear W20879181962018 @default.
- W2087918196 countsByYear W20879181962021 @default.
- W2087918196 crossrefType "proceedings-article" @default.
- W2087918196 hasAuthorship W2087918196A5007833886 @default.
- W2087918196 hasAuthorship W2087918196A5031174137 @default.
- W2087918196 hasAuthorship W2087918196A5046362615 @default.
- W2087918196 hasConcept C105795698 @default.
- W2087918196 hasConcept C11413529 @default.
- W2087918196 hasConcept C119599485 @default.
- W2087918196 hasConcept C119857082 @default.
- W2087918196 hasConcept C124101348 @default.
- W2087918196 hasConcept C126255220 @default.
- W2087918196 hasConcept C127413603 @default.
- W2087918196 hasConcept C134306372 @default.
- W2087918196 hasConcept C136764020 @default.
- W2087918196 hasConcept C147764199 @default.
- W2087918196 hasConcept C148483581 @default.
- W2087918196 hasConcept C154945302 @default.
- W2087918196 hasConcept C202615002 @default.
- W2087918196 hasConcept C203868755 @default.
- W2087918196 hasConcept C2776135515 @default.
- W2087918196 hasConcept C33923547 @default.
- W2087918196 hasConcept C37616216 @default.
- W2087918196 hasConcept C41008148 @default.
- W2087918196 hasConcept C78600449 @default.
- W2087918196 hasConcept C83546350 @default.
- W2087918196 hasConceptScore W2087918196C105795698 @default.
- W2087918196 hasConceptScore W2087918196C11413529 @default.
- W2087918196 hasConceptScore W2087918196C119599485 @default.
- W2087918196 hasConceptScore W2087918196C119857082 @default.
- W2087918196 hasConceptScore W2087918196C124101348 @default.
- W2087918196 hasConceptScore W2087918196C126255220 @default.
- W2087918196 hasConceptScore W2087918196C127413603 @default.
- W2087918196 hasConceptScore W2087918196C134306372 @default.
- W2087918196 hasConceptScore W2087918196C136764020 @default.
- W2087918196 hasConceptScore W2087918196C147764199 @default.
- W2087918196 hasConceptScore W2087918196C148483581 @default.
- W2087918196 hasConceptScore W2087918196C154945302 @default.
- W2087918196 hasConceptScore W2087918196C202615002 @default.
- W2087918196 hasConceptScore W2087918196C203868755 @default.
- W2087918196 hasConceptScore W2087918196C2776135515 @default.
- W2087918196 hasConceptScore W2087918196C33923547 @default.
- W2087918196 hasConceptScore W2087918196C37616216 @default.
- W2087918196 hasConceptScore W2087918196C41008148 @default.
- W2087918196 hasConceptScore W2087918196C78600449 @default.
- W2087918196 hasConceptScore W2087918196C83546350 @default.
- W2087918196 hasLocation W20879181961 @default.
- W2087918196 hasOpenAccess W2087918196 @default.
- W2087918196 hasPrimaryLocation W20879181961 @default.
- W2087918196 hasRelatedWork W2122825543 @default.
- W2087918196 hasRelatedWork W2147626660 @default.
- W2087918196 hasRelatedWork W2771997889 @default.
- W2087918196 hasRelatedWork W2910575305 @default.
- W2087918196 hasRelatedWork W2991486385 @default.
- W2087918196 hasRelatedWork W3105245600 @default.
- W2087918196 hasRelatedWork W3118634075 @default.
- W2087918196 hasRelatedWork W4284963956 @default.
- W2087918196 hasRelatedWork W4315927502 @default.
- W2087918196 hasRelatedWork W4319315694 @default.
- W2087918196 isParatext "false" @default.
- W2087918196 isRetracted "false" @default.
- W2087918196 magId "2087918196" @default.
- W2087918196 workType "article" @default.