A Comparison between the Performance of Feed Forward Neural Networks and the Supervised Growing Neural Gas Algorithm
نویسندگان
چکیده
AI 057 Abstract The Supervised Growing Neural Gas algorithm (SGNG) provides an interesting alternative to standard multilayer perceptrons (MLP). A comparison is drawn between the performance of SGNG and MLP in the domain of function mapping. A further eld of interest is classiication 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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