Abstract
This article describes recent improvements of an original neural network building method which could be applied in the particular case of 2 input neurones. After a brief recall of the main building principles of a neural net, authors introduce the capability for a neurone to receive more than 2 inputs. Two problems then arise: how to chose the input number of a neurone, and what becomes of the decision rule of a neurone? Treating these problems leads to an original feature selection method (based on genetic algorithms) and leads to adapt a linear discrimination algorithm to non separable problems. Experimental results for a handwritten digit recognition problem confirm the efficiency of the method.
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Ribert, A., Stocker, E., Ennaji, A., Lecourtier, Y. (1999). Classification and feature selection by a self-organizing neural network. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098223
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DOI: https://doi.org/10.1007/BFb0098223
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