title: Evolutionary q-Gaussian radial basis function neural networks for multiclassification.
abstract: This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overall performance, an experimental study with sixteen data sets taken from the UCI repository is presented. The q-Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR). The results show that the q-Gaussian model can be considered very competitive with the other classification methods.
authors.affiliation: Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd floor, 14074-Córdoba, Spain. i22fenaf@uco.es
authors.forename: Francisco
authors.initials: F
authors.lastname: Fernández-Navarro
authors.forename: César
authors.initials: C
authors.lastname: Hervás-Martínez
authors.forename: P A
authors.initials: PA
authors.lastname: Gutiérrez
authors.forename: M
authors.initials: M
authors.lastname: Carbonero-Ruz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D016011:Normal Distribution