Mostrar el registro sencillo del ítem
| dc.contributor.author | Cano, Rosa | |
| dc.contributor.author | González Arrieta, María Angélica | |
| dc.contributor.author | De Paz, Juan F. | |
| dc.contributor.author | Rodríguez González, Sara | |
| dc.date.accessioned | 2017-09-06T09:15:06Z | |
| dc.date.available | 2017-09-06T09:15:06Z | |
| dc.date.issued | 2009 | |
| dc.identifier.citation | Intelligent Data Engineering and Automated Learning - IDEAL 2009 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5788, pp. 727-734. | |
| dc.identifier.isbn | 978-3-642-04393-2 (Print) / 978-3-642-04394-9 (Online) | |
| dc.identifier.issn | 0302-9743 (Print) / 1611-3349 (Online) | |
| dc.identifier.uri | http://hdl.handle.net/10366/134946 | |
| dc.description.abstract | This paper presents a multiagent architecture constructed for learning from the interaction between the atmosphere and the ocean. The ocean surface and the atmosphere exchange carbon dioxide, and this process is modeled by means of a multiagent system with learning capabilities. The proposed multiagent architecture incorporates CBR-agents to monitor the parameters that affect the interaction and to facilitate the creation of models. The system has been tested and this paper presents the results obtained. | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | |
| dc.publisher | Springer Science + Business Media | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Unported | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ | |
| dc.subject | Computer Science | |
| dc.title | A Multi-agent System to Learn from Oceanic Satellite Image Data | |
| dc.type | info:eu-repo/semantics/article | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
BISITE. Congresos [232]
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivs 3.0 Unported