<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-17T21:25:38Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/9943" metadataPrefix="mods">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/9943</identifier><datestamp>2025-01-17T01:05:29Z</datestamp><setSpec>com_10259_4219</setSpec><setSpec>com_10259_5086</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_4220</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Garrido Labrador, José Luis</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Maudes Raedo, Jesús M.</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Rodríguez Diez, Juan José</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>García Osorio, César</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2025-01-16T11:19:52Z</mods:dateAvailable>
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<mods:extension>
<mods:dateAccessioned encoding="iso8601">2025-01-16T11:19:52Z</mods:dateAccessioned>
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<mods:originInfo>
<mods:dateIssued encoding="iso8601">2025-01</mods:dateIssued>
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<mods:identifier type="issn">2352-7110</mods:identifier>
<mods:identifier type="uri">http://hdl.handle.net/10259/9943</mods:identifier>
<mods:identifier type="doi">10.1016/j.softx.2024.102024</mods:identifier>
<mods: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.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
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<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by/4.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Atribución 4.0 Internacional</mods:accessCondition>
<mods:subject>
<mods:topic>Semi-supervised learning</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Python library</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Self-training</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Co-training</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Restricted set classification</mods:topic>
</mods:subject>
<mods:titleInfo>
<mods:title>SSLearn: A Semi-Supervised Learning library for Python</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/article</mods:genre>
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