| Título | Representative datasets for neural networks |
|---|---|
| Autores | Gonzalez-Diaz R. , PALUZO HIDALGO, EDUARDO, Gutiérrez-Naranjo M.A. |
| Publicación externa | No |
| Medio | Electron. Notes Discrete Math. |
| Alcance | Article |
| Naturaleza | Científica |
| Cuartil SJR | 3 |
| Impacto SJR | 0.347 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049891030&doi=10.1016%2fj.endm.2018.06.016&partnerID=40&md5=4346596ada3a490c4c4caf1b3b2e2d22 |
| Fecha de publicacion | 01/01/2018 |
| Scopus Id | 2-s2.0-85049891030 |
| DOI | 10.1016/j.endm.2018.06.016 |
| Abstract | Neural networks present big popularity and success in many fields. The large training time process problem is a very important task nowadays. In this paper, a new approach to get over this issue based on reducing dataset size is proposed. Two algorithms covering two different shape notions are shown and experimental results are given. © 2018 Elsevier B.V. |
| Palabras clave | Algorithm; neural networks; proximity graph; a-shape |
| Miembros de la Universidad Loyola |