Abstract

The quality of Support Vector Machines binary classification of spatial environmental data is evaluated with geostatistical nonparametrtic conditional stochastic simulations. Equally probable realizations are generated and compared with SVM. Case study is based on the classification of porosity data. Results obtained confirm the efficiency of the SVM binary classification of spatial data

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