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Similarity networks for heterogeneous data
Belanche Muñoz, Luis Antonio; Hernández González, Jerónimo
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing
A two-layer neural network is developed in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted logistic 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 involves 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.
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Neural networks (Computer science)
Xarxes neuronals (Informàtica)
Attribution-NonCommercial-NoDerivs 3.0 Spain

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