RT info:eu-repo/semantics/article
T1 SSLearn: A Semi-Supervised Learning library for Python
A1 Garrido Labrador, José Luis
A1 Maudes Raedo, Jesús M.
A1 Rodríguez Diez, Juan José
A1 García Osorio, César
K1 Semi-supervised learning
K1 Python library
K1 Self-training
K1 Co-training
K1 Restricted set classification
K1 Informática
K1 Computer science
K1 Educación
K1 Education
AB 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.
PB Elsevier
SN 2352-7110
YR 2025
FD 2025-01
LK http://hdl.handle.net/10259/9943
UL http://hdl.handle.net/10259/9943
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 21-ene-2025