dc.contributor.author | Román Txopitea, Ibai | |
dc.contributor.author | Santana Hermida, Roberto | |
dc.contributor.author | Mendiburu Alberro, Alexander | |
dc.contributor.author | Lozano Alonso, José Antonio | |
dc.date.accessioned | 2021-12-09T09:18:56Z | |
dc.date.available | 2021-12-09T09:18:56Z | |
dc.date.issued | 2021-10-28 | |
dc.identifier.citation | Neurocomputing 462 : 426-439 (2021) | es_ES |
dc.identifier.issn | 0925-2312 | |
dc.identifier.issn | 1872-8286 | |
dc.identifier.uri | http://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.sponsorship | This 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.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2019-104966 GB-I00 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/SEV-2017-0718 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | evolutionary search | es_ES |
dc.subject | Gaussian processes | es_ES |
dc.subject | genetic programming | es_ES |
dc.subject | kernel learning | es_ES |
dc.subject | time series extrapolation | es_ES |
dc.title | Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation | es_ES |
dc.type | info:eu-repo/semantics/article | es_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.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www-sciencedirect-com.ehu.idm.oclc.org/science/article/pii/S0925231221012042?via%3Dihub | es_ES |
dc.identifier.doi | 10.1016/j.neucom.2021.08.020 | |
dc.departamentoes | Arquitectura y Tecnología de Computadores | es_ES |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputagailuen Arkitektura eta Teknologia | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |