| 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 | 0.322 |
| 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 distribution; Image segmentation; Radial basis function networks; Neural networks |
| Miembros de la Universidad Loyola |