Neural Networks for Complex Data
| dc.contributor.author | Cottrell, Marie | |
| dc.contributor.author | Olteanu, Madalina | |
| dc.contributor.author | Rossi, Fabrice | |
| dc.contributor.author | Rynkiewicz, Joseph | |
| dc.contributor.author | Villa-Vialaneix, Nathalie | |
| dc.date.accessioned | 2018-01-08T09:16:10Z | |
| dc.date.available | 2018-01-08T09:16:10Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Université Paris 1. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11315 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.title | Neural Networks for Complex Data | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 380 | |
| gi.citation.startPage | 373 |