| 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 |
| Publicación externa |
No |
| Medio |
Lect. Notes Comput. Sci. |
| Alcance |
Conference Paper |
| Naturaleza |
Científica |
| Cuartil JCR |
4 |
| Cuartil SJR |
2 |
| Impacto SJR |
0.32200 |
| 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 |
| ISI |
000286905700035 |
| 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 |
| Miembros de la Universidad Loyola |
|
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