Abstract
We discuss alternative norms to train Neural Networks (NNs). We focus on the so called Multilayer Perceptrons (MLPs). To achieve this we rely on a Genetic Algorithm called an Eclectic GA (EGA). By using the EGA we avoid the drawbacks of the standard training algorithm in this sort of NNs: the backpropagation algorithm. We define four measures of distance: a) The mean exponential error (MEE), b) The mean absolute error (MAE), c) The maximum square error (MSE) and d) The maximum (supremum) absolute error (SAE). We analyze the behavior of an MLP NN on two kinds of problems: Classification and Forecasting. We discuss the results of applying an EGA to train the NNs and show that alternative norms yield better results than the traditional RMS norm.
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Kuri Morales, A. (2000). Training Neural Networks Using Non-standard Norms – Preliminary Results. In: Cairó, O., Sucar, L.E., Cantu, F.J. (eds) MICAI 2000: Advances in Artificial Intelligence. MICAI 2000. Lecture Notes in Computer Science(), vol 1793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720076_32
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DOI: https://doi.org/10.1007/10720076_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67354-5
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