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dc.contributor.authorRomán Txopitea, Ibai
dc.contributor.authorSantana Hermida, Roberto ORCID
dc.contributor.authorMendiburu Alberro, Alexander
dc.contributor.authorLozano Alonso, José Antonio
dc.date.accessioned2021-12-09T09:18:56Z
dc.date.available2021-12-09T09:18:56Z
dc.date.issued2021-10-28
dc.identifier.citationNeurocomputing 462 : 426-439 (2021)es_ES
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.urihttp://hdl.handle.net/10810/54393
dc.description.abstract[EN]Choosing the best kernel is crucial in many Machine Learning applications. Gaussian Processes are a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Processes literature, kernels have usually been either ad hoc designed, selected from a predefined set, or searched for in a space of compositions of kernels which have been defined a priori. In this paper, we propose a Genetic Programming algorithm that represents a kernel function as a tree of elementary mathematical expressions. By means of this representation, a wider set of kernels can be modeled, where potentially better solutions can be found, although new challenges also arise. The proposed algorithm is able to overcome these difficulties and find kernels that accurately model the characteristics of the data. This method has been tested in several real-world time series extrapolation problems, improving the state-of-the-art results while reducing the complexity of the kernels.es_ES
dc.description.sponsorshipThis work has been supported by the Spanish Ministry of Science and Innovation (project PID2019-104966 GB-I00) , and the Basque Government (projects KK-2020/00049 and IT1244-19, and ELKARTEK program) . Jose A. Lozano is also supported by BERC 2018-2021 (Basque government) and BCAM Severo Ochoa accred-itation SEV-2017-0718 (Spanish Ministry of Science and Innovation) .es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2019-104966 GB-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/SEV-2017-0718es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectevolutionary searches_ES
dc.subjectGaussian processeses_ES
dc.subjectgenetic programminges_ES
dc.subjectkernel learninges_ES
dc.subjecttime series extrapolationes_ES
dc.titleEvolving Gaussian process kernels from elementary mathematical expressions for time series extrapolationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder(c) 2021 The Authors. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www-sciencedirect-com.ehu.idm.oclc.org/science/article/pii/S0925231221012042?via%3Dihubes_ES
dc.identifier.doi10.1016/j.neucom.2021.08.020
dc.departamentoesArquitectura y Tecnología de Computadoreses_ES
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputagailuen Arkitektura eta Teknologiaes_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


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(c) 2021 The Authors. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as (c) 2021 The Authors. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).