The research considers the problem of spatial data classification
using machine learning algorithms: probabilistic neural networks
(PNN) and support vector machines (SVM). As a benchmark model simple
k-nearest neighbor algorithm is considered. PNN is a neural network
reformulation of well known nonparametric principles of probability
density modeling using kernel density estimator and Bayesian optimal
or maximum a posteriori decision rules. PNN is well suited to problems
where not only predictions but also quantification of accuracy and
integration of prior information are necessary. An important property
of PNN is that they can be easily used in decision support systems
dealing with problems of automatic classification. Support vector
machine is an implementation of the principles of statistical learning
theory for the classification tasks. Recently they were successfully
applied for different environmental topics: classification of soil
types and hydro-geological units, optimization of monitoring networks,
susceptibility mapping of natural hazards. In the present paper both
simulated and real data case studies (low and high dimensional) are
considered. The main attention is paid to the detection and learning
of spatial patterns by the algorithms applied.