In this paper we present a realization of the informationbottleneck-paradigm by means of an improved counter propagation network. It combines an unsupervised vector quantizer for data compression with a subsequent supervised learning vector quantization model. The approach is mathematically justified and yields an interpretable model for classification under the constraint of data compression, which is not longer independently learned from the classification task.* M.K., M.M.B. and D.S. were supported by grants of the European Social Fund (ESF) for a Young Researcher Group 'MaLeKITA' and a PhD grant.

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