Abstract
The Supervised Growing Neural Gas algorithm (SGNG) provides an interesting alternative to standard Multi-Layer Perceptrons (MLP). A comparison is drawn between the performance of SGNG and MLP in the domain of function mapping. A further field of interest is classification power, which has been investigated with real data taken by PS197 at CERN. The characteristics of the two network models will be discussed from a practical point of view as well as their advantages and disadvantages.
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