| Title |
Evolutionary q-Gaussian radial basis functions for binary-classification |
| Authors |
FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervás-Martínez C. , Gutiérrez P.A. , Cruz-Ramírez M. , CARBONERO RUZ, MARIANO |
| External publication |
No |
| Means |
Lecture Notes in Computer Science |
| Scope |
Conference Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
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 |
| Publication date |
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. |
| Keywords |
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 |
| Universidad Loyola members |
|
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