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This paper develops a two-layer neural network in which the neuron modelcomputes a user-defined similarity function between inputs and weights. Theneuron transfer function is formed by composition of an adapted logisticfunction with the mean of the partial input-weight similarities. The resultingneuron model is capable of dealing directly with variables of potentiallydifferent nature (continuous, fuzzy, ordinal, categorical). There is alsoprovision for missing values. The network is trained using a two-stageprocedure very similar to that used to train a radial basis function (RBF)neural network. The network is compared to two types of RBF networks in anon-trivial dataset: the Horse Colic problem, taken as a case study andanalyzed in detail.

Similarity networks for classification: a case study in the Horse Colic problem