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A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data

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Bibliographic Details
Published in:International Journal of Intelligent Systems and Applications
Format: Online Article RSS Article
Published: 2026
Subjects:
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container_title International Journal of Intelligent Systems and Applications
description
discipline_display Artificial Intelligence
discipline_facet Artificial Intelligence
format Online Article
RSS Article
genre Journal Article
id rss_article:83308
institution FRELIP
journal_source_facet International Journal of Intelligent Systems and Applications
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data
Artificial Intelligence
General
Artificial Intelligence
sub_discipline_display General
sub_discipline_facet General
subject_display Artificial Intelligence
General
Artificial Intelligence
Artificial Intelligence
General
Artificial Intelligence
subject_facet Artificial Intelligence
General
Artificial Intelligence
title A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data
title_auth A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data
title_full A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data
title_fullStr A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data
title_full_unstemmed A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data
title_short A Hybrid Active and Semi-Supervised Learning Framework for Classification with Minimal Labeled Data
title_sort a hybrid active and semi-supervised learning framework for classification with minimal labeled data
topic Artificial Intelligence
General
Artificial Intelligence
url https://www.mecs-press.org/ijisa/ijisa-v18-n3/v18n3-5.html