Similarity networks for heterogeneous data
Luis Antonio Belanche Muñoz, Jerónimo Hernández González · The European Symposium on Artificial Neural Networks · 2012
A two-layer neural network is developed in which the neu- ron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted lo- gistic function with the mean of the partial input-weight similarities. The model is capable of dealing directly with variables of potentially different nature (continuous, ordinal, categorical); there is also provision for missing values. The network is trained using a fast two-stage procedure and in- volves the setting of only one parameter. In our experiments, the network achieves slightly superior performance on a set of challenging problems with respect to both RBF nets and RBF-kernel SVMs.