<?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>2026-06-30T21:43:55Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/9943" metadataPrefix="qdc">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><qdc:qualifieddc xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
<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>
<dcterms: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.</dcterms:abstract>
<dcterms:dateAccepted>2025-01-16T11:19:52Z</dcterms:dateAccepted>
<dcterms:available>2025-01-16T11:19:52Z</dcterms:available>
<dcterms:created>2025-01-16T11:19:52Z</dcterms:created>
<dcterms:issued>2025-01</dcterms:issued>
<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>
</qdc:qualifieddc></metadata></record></GetRecord></OAI-PMH>