Abstract
This paper deals with a new scheme of distributed classifier based on a particular formal neuron named “yprel”. The main characteristics of the proposed approach are: (i) a classifier is a set of interconnected and cooperating networks; (ii) the distributed resolution strategy emerges from the individual network classification behaviors during the incremental building phase of the classifier; (iii) each neuron is able to come to classification decisions about some elements and to communicate them; (iv) the network architectures and the interconnexion links between the networks are not a priori chosen, but get, themselves organized thanks to an incremental and competitive learning between the decision-making neurons.
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© 1999 Springer-Verlag Berlin Heidelberg
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Stocker, E., Ribert, A., Lecourtier, Y. (1999). Self-organizing yprel network population for distributed classification problem solving. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100552
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DOI: https://doi.org/10.1007/BFb0100552
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Print ISBN: 978-3-540-66068-2
Online ISBN: 978-3-540-48772-2
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