Ensemble methods for environmental data modelling with support vector regression

Détails

ID Serval
serval:BIB_CE9C4C66B8C6
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Ensemble methods for environmental data modelling with support vector regression
Titre de la conférence
European Colloquium on Theoretical and Quantitative Geography, Montreux, Switzerland, 3-7 September
Auteur⸱e⸱s
Ratle F., Tuia A.
Statut éditorial
Publié
Date de publication
2007
Langue
anglais
Notes
Ratle2007c
Résumé
This paper investigates the use of ensemble of predictors in order
to improve the performance of spatial prediction methods. Support
vector regression (SVR), a popular method from the field of statistical
machine learning, is used. Several instances of SVR are combined
using different data sampling schemes (bagging and boosting). Bagging
shows good performance, and proves to be more computationally efficient
than training a single SVR model while reducing error. Boosting,
however, does not improve results on this specific problem.
Création de la notice
25/11/2013 17:18
Dernière modification de la notice
20/08/2019 15:49
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