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dc.contributor.authorBueno-Crespo, Andrés
dc.contributor.authorMenchón Lara, Rosa María
dc.contributor.authorMartínez-España, Raquel
dc.contributor.authorSancho-Gómez, José-Luis
dc.date.accessioned2024-02-07T08:57:58Z
dc.date.available2024-02-07T08:57:58Z
dc.date.issued2017
dc.identifier.citationFront Neuroinform,11:39, 2017es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/65877
dc.description.abstractFaced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL), which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one. This paper presents a new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections. In this sense, the proposed method realizes a complete design of the MTL schemes. The method is simple and uses the advantages of the Extreme Learning Machine to automatically design a MTL machine, eliminating those factors that hinder, or do not benefit, the learning process of the main task. This architecture is unique and it is obtained without testing/error methodologies that increase the computational complexity. The results obtained over several real problems show the good performances of the designed networks with this method.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.titleBioinspired Architecture Selection for Multitask Learninges
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.3389/fninf.2017.00039es
dc.identifier.publicationtitleFrontiers in Neuroinformaticses
dc.identifier.publicationvolume11es
dc.peerreviewedSIes
dc.identifier.essn1662-5196es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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