Abstract
In this paper, we have developed a parallel approach for minimizing the projection error in Sammon’s mapping applied in combination with the self-organizing map (SOM). In the so-called integrated mapping, Sammon’s algorithm takes into account the learning flow of the self-organizing neural network. As a final result in the integrated mapping, we need to visualize the neurons-winners of the SOM. The criterion of visualization quality is the projection error of Sammon’s mapping.
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Dzemyda, G., Kurasova, O. (2004). Parallelization of the SOM-Based Integrated Mapping. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_22
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DOI: https://doi.org/10.1007/978-3-540-24844-6_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
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