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

Autor:	Belanche, Lluís, Hernández, Jerónimo
Rok vydání:	2014
Předmět:	Computer Science - Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	This paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input-weight similarities. The resulting neuron model is capable of dealing directly with variables of potentially different nature (continuous, fuzzy, ordinal, categorical). There is also provision for missing values. The network is trained using a two-stage procedure 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 a non-trivial dataset: the Horse Colic problem, taken as a case study and analyzed in detail. Comment: 16 pages, 1 figure Universitat Polit\`ecnica de Catalunya preprint
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1403.4540 Zobrazit plný text záznamu View this record from Arxiv