Abstract. In this work we introduce a method for classification and visualization. In contrast to simultaneous methods like e.g. Kohonen SOM this new approach, called KMC/EDAM, runs through two stages. In the first stage the data is clustered by classical methods like K-means clustering. In the second stage the centroids of the obtained clusters are visualized in a fixed target space which is directly comparable to that of SOM.
Investigating a data set containing different sounds of several instruments suggests that local modelling may be a promising approach to take into account different timbre characteristics of different instruments. For this reason, some basic ideas towards a local modelling are realized in this report yielding a framework for further studies.
A lot of alternatives and constraints have been proposed in order to improve the Fisher criterion. But most of them are not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound for the error rate a criterion is developed which can improve the classification performance.
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