Abstract
Neural networks have emerged as an efficient method to complement more traditional approaches, in particular in situations where a design of algorithms from first principles becomes too costly or fails due to insufficient information about e.g. the statistics of a problem. However, as the problems to which neural networks are applied become more demanding, such as in machine vision, the choice of an adequate network architecture becomes more and more a crucial issue. This is particularly true for larger applications, where the actions of several neural networks need to be coherently integrated into a larger system. Unfortunately, systematic investigations of this issue are just beginning to appear in the literature (for an interesting approach, see e.g. [2, 12]) and results are still rather sparse.
This work was supported by the German Ministry of Research and Technology (BMFT), Grant No. ITN9104AO. Any responsibility for the contents of this publication is with the authors.
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Littmann, E., Meyering, A., Ritter, H. (1992). Cascaded and Parallel Neural Network Architectures for Machine Vision — A Case Study. In: Fuchs, S., Hoffmann, R. (eds) Mustererkennung 1992. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77785-1_10
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DOI: https://doi.org/10.1007/978-3-642-77785-1_10
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