Forventes på lager: 10-12-2019
ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series,etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments.
| Forlag | Springer Nature Switzerland AG |
| Type | Bog |
| Format | Paperback / softback |
| Sprog | Engelsk |
| Udgave | Softcover Reprint of the Original 1st 2019 ed. |
| Udgivelsesdato | 10-12-2019 |
| Første udgivelsesår | 2019 |
| Serie | Proceedings in Adaptation, Learning and Optimization |
| Illustrationer | 130 Illustrations, black and white |
| Fagredaktør | Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse |
| Originalsprog | Switzerland |
| Sideantal | 340 |
| Indbinding | Paperback / softback |
| Forlag | Springer Nature Switzerland AG |
| Sideoplysninger | 340 pages, 130 Illustrations, black and white |
| Mål | 235 x 155 |
| ISBN-13 / EAN-13 | 9783030131821 |