Ensemble methods for environmental data modelling with support vector regression

Details

Serval ID
serval:BIB_CE9C4C66B8C6
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Ensemble methods for environmental data modelling with support vector regression
Title of the conference
European Colloquium on Theoretical and Quantitative Geography, Montreux, Switzerland, 3-7 September
Author(s)
Ratle F., Tuia A.
Publication state
Published
Issued date
2007
Language
english
Notes
Ratle2007c
Abstract
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.
Create date
25/11/2013 17:18
Last modification date
20/08/2019 15:49
Usage data