Developments and Applications of the Self-organizing Map and Related Algorithms

In this paper the basic principles and developments of an unsu-pervised learning algorithm, the Self-Organizing Map (SOM) and a supervised learning algorithm, the Learning Vector Quantization (LVQ) are explained. Some practical applications of the algorithms in data analysis, data visual-ization and pattern recognition tasks are mentioned. In the end of the paper new results are reported about increased error tolerance in the transmission of vector quantized images, provided by the topological ordering of codewords by the SOM algorithm.