dc.contributor.author | Garrido Labrador, José Luis | |
dc.contributor.author | Maudes Raedo, Jesús M. | |
dc.contributor.author | Rodríguez Diez, Juan José | |
dc.contributor.author | García Osorio, César | |
dc.date.accessioned | 2025-01-16T11:19:52Z | |
dc.date.available | 2025-01-16T11:19:52Z | |
dc.date.issued | 2025-01 | |
dc.identifier.issn | 2352-7110 | |
dc.identifier.uri | http://hdl.handle.net/10259/9943 | |
dc.description.abstract | SSLearn is an open-source Python-based library that advances semi-supervised learning (SSL) with a focus on wrapper algorithms and restricted set classification (RSC), a novel paradigm. It fosters innovation by allowing researchers to modify methods or create new ones, facilitating access to state-of-the-art algorithms and comparative studies. As the only library incorporating RSC for constrained classification, SSLearn fills an important gap in SSL tools. Fully compatible with Scikit-Learn, it integrates seamlessly into research workflows, lowering the barrier to entry to SSL and catalyzing its adoption in diverse domains. This makes SSLearn a critical resource for advancing SSL research and applications. | en |
dc.description.sponsorship | This work was supported through the Junta de Castilla 𝑦� León (JCyL) (regional government) under project BU055P20 (JCyL/FEDER, UE), the Spanish Ministry of Science and Innovation under project PID2020- 119894GB-I00 co-financed through European Union FEDER funds, and project TED2021-129485B-C43 funded by MCIN/AEI/ 10.13039/501 100011033 and the European Union NextGenerationEU/PRTR. J.L. Garrido-Labrador is supported through Consejería de Educación of the Junta de Castilla 𝑦� León and the European Social Fund through a pre-doctoral grant EDU/875/2021 (Spain). | es |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | SoftwareX. 2025, V. 29, p. 102024 | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Semi-supervised learning | en |
dc.subject | Python library | en |
dc.subject | Self-training | en |
dc.subject | Co-training | en |
dc.subject | Restricted set classification | en |
dc.subject.other | Informática | es |
dc.subject.other | Computer science | en |
dc.subject.other | Educación | es |
dc.subject.other | Education | en |
dc.title | SSLearn: A Semi-Supervised Learning library for Python | en |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.relation.publisherversion | https://doi.org/10.1016/j.softx.2024.102024 | es |
dc.identifier.doi | 10.1016/j.softx.2024.102024 | |
dc.journal.title | SoftwareX | es |
dc.volume.number | 29 | es |
dc.page.initial | 102024 | es |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
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