Statistical learning theory for geospatial data. Case study: Aral sea

Détails

ID Serval
serval:BIB_82C513F40F89
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
Titre
Statistical learning theory for geospatial data. Case study: Aral sea
Titre de la conférence
European colloquium on Theoretical and Quantitative Geography, Tomar, Portugal
Auteur(s)
Kanevski M., Pozdnukhov A., Tonini M., Maignan M., Motelica M., Savelieva E.
Statut éditorial
Publié
Date de publication
2005
Pages
161-162
Langue
anglais
Notes
Kanevski2005
Résumé
In recent years there has been an explosive growth in the development
of adaptive and data driven methods. One of the efficient and data-driven
approaches is based on statistical learning theory (Vapnik 1998).
The theory is based on Structural Risk Minimisation (SRM) principle
and has a solid statistical background. When applying SRM we are
trying not only to reduce training error ? to fit the available data
with a model, but also to reduce the complexity of the model and
to reduce generalisation error. Many nonlinear learning procedures
recently developed in neural networks and statistics can be understood
and interpreted in terms of the structural risk minimisation inductive
principle. A recent methodology based on SRM is called Support Vector
Machines (SVM). At present SLT is still under intensive development
and SVM find new areas of application (www.kernel-machines.org).
SVM develop robust and non linear data models with excellent generalisation
abilities that is very important both for monitoring and forecasting.
SVM are extremely good when input space is high dimensional and training
data set i not big enough to develop corresponding nonlinear model.
Moreover, SVM use only support vectors to derive decision boundaries.
It opens a way to sampling optimization, estimation of noise in data,
quantification of data redundancy etc. Presentation of SVM for spatially
distributed data is given in (Kanevski and Maignan 2004).
Création de la notice
25/11/2013 18:18
Dernière modification de la notice
03/03/2018 18:51
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