Título Evolutionary q-Gaussian radial basis functions for binary-classification
Autores FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervás-Martínez C., Gutiérrez P.A., Cruz-Ramírez M., CARBONERO RUZ, MARIANO, CARBONERO RUZ, MARIANO, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS
Publicación externa No
Medio Lect. Notes Comput. Sci.
Alcance Conference Paper
Naturaleza Científica
Cuartil JCR 4
Cuartil SJR 2
Impacto SJR 0.32200
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954602192&doi=10.1007%2f978-3-642-13803-4_35&partnerID=40&md5=847af9394dd7e1fbf3c1369a8d799822
Fecha de publicacion 01/01/2010
Scopus Id 2-s2.0-77954602192
DOI 10.1007/978-3-642-13803-4_35
Abstract This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means a real parameter q, named q-Gaussian RBFNN. The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop?+ algorithm as the local improvement procedure. In order to test its overall performance, an experimental study with eleven datasets, taken from the UCI repository is presented. The RBFNN with the q-Gaussian is compared to RBFNN with Gaussian, Cauchy and Inverse Multiquadratic RBFs. © 2010 Springer-Verlag.
Palabras clave Data sets; Experimental studies; Gaussian radial basis functions; Gaussians; Hybrid algorithms; Multiquadratics; Radial basis function neural networks; Radial basis functions; UCI repository; Gaussian
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