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<dc:title>SSLearn: A Semi-Supervised Learning library for Python</dc:title>
<dc:creator>Garrido Labrador, José Luis</dc:creator>
<dc:creator>Maudes Raedo, Jesús M.</dc:creator>
<dc:creator>Rodríguez Diez, Juan José</dc:creator>
<dc:creator>García Osorio, César</dc:creator>
<dc:subject>Semi-supervised learning</dc:subject>
<dc:subject>Python library</dc:subject>
<dc:subject>Self-training</dc:subject>
<dc:subject>Co-training</dc:subject>
<dc:subject>Restricted set classification</dc:subject>
<dc:description>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.</dc:description>
<dc:date>2025-01-16T11:19:52Z</dc:date>
<dc:date>2025-01-16T11:19:52Z</dc:date>
<dc:date>2025-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:identifier>2352-7110</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/9943</dc:identifier>
<dc:identifier>10.1016/j.softx.2024.102024</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>SoftwareX. 2025, V. 29, p. 102024</dc:relation>
<dc:relation>https://doi.org/10.1016/j.softx.2024.102024</dc:relation>
<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:rights>Atribución 4.0 Internacional</dc:rights>
<dc:publisher>Elsevier</dc:publisher>
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