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dc.creatorDawson, Christian
dc.date.accessioned2021-01-20T20:21:31Z
dc.date.available2021-01-20T20:21:31Z
dc.date.created2016-11-11
dc.identifier.isbn978-3-038-42270-9
dc.identifier.isbn978-3-038-42271-6
dc.identifier.otherhttps://www.mdpi.com/books/pdfview/book/236
dc.identifier.urihttp://hdl.handle.net/20.500.12010/16788
dc.format.extent260 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherMDPI - Multidisciplinary Digital Publishing Institutespa
dc.subjectRedes neuronales artificialesspa
dc.titleApplied Artificial Neural Networksspa
dc.subject.lembAprendizaje automáticospa
dc.subject.lembMinería de datosspa
dc.subject.lembRedes neuronalesspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doi10.3390/books978-3-03842-271-6
dc.description.abstractenglishSince their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves—in terms of training, topologies, types, etc.—a similar amount of work has examined their application to a whole host of real-world problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, hydrology, etc.This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by-nc-nd/4.0/legalcode


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