Abstract
A new connectionist model, called Switching Neural Network (SNN), for the solution of classification problems is presented. SNN includes a first layer containing a particular kind of A/D converters, called latticizers, that suitably transform input vectors into binary strings. Then, the subsequent two layers of an SNN realize a positive Boolean function that solves in a lattice domain the original classification problem.
Every function realized by an SNN can be written in terms of intelligible rules. Training can be performed by adopting a proper method for positive Boolean function reconstruction, called Shadow Clustering (SC). Simulation results obtained on the StatLog benchmark show the good quality of the SNNs trained with SC.
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© 2006 Springer-Verlag Berlin Heidelberg
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Muselli, M. (2006). Switching Neural Networks: A New Connectionist Model for Classification. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_4
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DOI: https://doi.org/10.1007/11731177_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
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