Artificial Neural Networks (ANN) are new
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.