Evaluation of SVM Binary Classification with Nonparametric Conditional Stochastic Simulations
نویسندگان
چکیده
The quality of Support Vector Machines (SVM) binary classification of spatial environmental data is evaluated with geostatistical nonparametric conditional stochastic simulations a spatial Monte Carlo model based on sequential indicator simulation algorithm. Equally probable realizations are generated and compared with SVM classification. Uncertainty of predictions is described by conditional standard deviations. Case study is based on the classification of porosity data. Only binary problem is considered. Results obtained confirm the efficiency of the SVM binary classification of spatial data.
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