dc.creator | Vasco Carofilis, Roberto A. | es |
dc.creator | Gutiérrez Naranjo, Miguel Ángel | es |
dc.creator | Cárdenas Montes, Miguel | es |
dc.date.accessioned | 2021-04-07T09:54:29Z | |
dc.date.available | 2021-04-07T09:54:29Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Vasco Carofilis, R.A., Gutiérrez Naranjo, M.Á. y Cárdenas Montes, M. (2020). PBIL for Optimizing Hyperparameters of Convolutional Neural Networks and STL Decomposition. En HAIS 2020: 15th International Conference on Hybrid Artificial Intelligence Systems (147-159), Gijón, España: Springer. | |
dc.identifier.isbn | 978-3-030-61704-2 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/106782 | |
dc.description.abstract | The optimization of hyperparameters in Deep Neural Net-works is a
critical task for the final performance, but it involves a high amount of subjective
decisions based on previous researchers’ expertise. This paper presents the
implementation of Population-based Incremen-tal Learning for the automatic
optimization of hyperparameters in Deep Learning architectures. Namely, the
proposed architecture is a combina-tion of preprocessing the time series input with
Seasonal Decomposition of Time Series by Loess, a classical method for decomposing
time series, and forecasting with Convolutional Neural Networks. In the past, this
combination has produced promising results, but penalized by an incre-mental
number of parameters. The proposed architecture is applied to the prediction of the
222Rn level at the Canfranc Underground Labora-tory (Spain). By predicting the lowlevel
periods of 222Rn, the potential contamination during the maintenance
operations in the experiments hosted in the laboratory could be minimized. In this
paper, it is shown that Population-based Incremental Learning can be used for the
choice of optimized hyperparameters in Deep Learning architectures with a reasonable
computational cost. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad MDM- 2015-0509 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | HAIS 2020: 15th International Conference on Hybrid Artificial Intelligence Systems (2020), pp. 147-159. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Hyperparameters optimization | es |
dc.subject | Convolutional Neural Networks (CNN) | es |
dc.subject | STL decomposition | es |
dc.subject | PBIL | es |
dc.subject | 222Rn measurements | es |
dc.subject | Canfranc Underground Laboratory | es |
dc.subject | Forecasting | es |
dc.title | PBIL for Optimizing Hyperparameters of Convolutional Neural Networks and STL Decomposition | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | MDM- 2015-0509 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-61705-9_13 | es |
dc.identifier.doi | 10.1007/978-3-030-61705-9_13 | es |
dc.publication.initialPage | 147 | es |
dc.publication.endPage | 159 | es |
dc.eventtitle | HAIS 2020: 15th International Conference on Hybrid Artificial Intelligence Systems | es |
dc.eventinstitution | Gijón, España | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |