Abstract
The solution of binary classification problems is obtained by employing a new learning method, called Hamming Clustering (HC). It is able to build in a constructive way a two-layer perceptron with binary weights, which can be easily implemented by means of conventional logical ports.
This technique generalizes the information contained in the given training set by combining input patterns that are close each other according to the Hamming distance. The output class is assigned in a competitive way, thus allowing the treatment of ambiguous samples.
The application of HC to the signal prediction in genomic sequences shows its ability to determine regularities in complex problems.
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© 1999 Springer-Verlag London Limited
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Muselli, M. (1999). Building Neural and Logical Networks with Hamming Clustering. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN Vietri-99. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0877-1_31
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DOI: https://doi.org/10.1007/978-1-4471-0877-1_31
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