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Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

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Published in:Journal of Machine Learning Research
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Machine Learning Research
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:4938
institution FRELIP
journal_source_facet Journal of Machine Learning Research
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
title_auth Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
title_full Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
title_fullStr Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
title_full_unstemmed Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
title_short Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
title_sort adaptive distributed kernel ridge regression: a feasible distributed learning scheme for data silos
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v26/23-0806.html