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Artificial Neural Networks (ANN) are new technologies for classification. ANN can process incomplete and imprecise data and detect non-linear relations in the data. ANN learning algorithms can be divided into 2 types, supervised and unsupervised. Neural networks learn in massively parallel and self-organizing way. Unsupervised learning neural networks, like Kohonen's self-organizing feature maps (Kohonen, 1989), learn the structure of high-dimensional data by mapping it on low-dimensional topologies, preserving the distribution and topology of the data. In this thesis Kohonen's self-organizing feature map is applied to the classification of a land cover data set. The data was collected from an existing expertly labeled database of land cover regions in Cambodia. Rule extraction derives land cover classes produced by self-organizing methods. Geological experts can use the rule generation algorithm of rule extraction produced by neural networks.

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