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| dc.contributor.author | Navarro Cáceres, María | |
| dc.contributor.author | González Arrieta, María Angélica | |
| dc.contributor.author | De Paz, Juan F. | |
| dc.contributor.author | Bajo Pérez, Javier | |
| dc.contributor.author | Rodríguez González, Sara | |
| dc.date.accessioned | 2017-09-06T09:17:16Z | |
| dc.date.available | 2017-09-06T09:17:16Z | |
| dc.date.issued | 2013/05 | |
| dc.identifier.citation | 10th International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2013). Advances in Intelligent Systems and Computing. Volumen 217, pp. 341-349. | |
| dc.identifier.isbn | 978-3-319-00550-8(Print) / 978-3-319-00551-5(Online) | |
| dc.identifier.issn | 2194-5357(Print) / 2194-5365(Online) | |
| dc.identifier.uri | http://dx.doi.org/10.1007/978-3-319-00551-5_42 | |
| dc.identifier.uri | http://hdl.handle.net/10366/135172 | |
| dc.description.abstract | The research offers a quite simple view of methods to classify edible and poisonous mushrooms. In fact, we are looking for not only classification methods but also for an application which supports experts’ decisions. To achieve our aim, we will study different structures of neural nets and learning algorithms, and select the best one, according to the test results. | |
| 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 | Automatic Prediction of Poisonous Mushrooms by Connectionist Systems | |
| dc.type | info:eu-repo/semantics/article | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess |
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