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Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging

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dc.contributor.authorWidmer, Christian
dc.contributor.authorKloft, Marius
dc.contributor.authorLou, Xinghua
dc.contributor.authorRätsch, Gunnar
dc.date.accessioned2018-01-08T09:17:01Z
dc.date.available2018-01-08T09:17:01Z
dc.date.issued2014
dc.description.abstractThe aim of multitask learning is to improve the generalization performance of a set of related tasks by exploiting complementary information about the tasks. In this paper, we review established approaches for regularization based multitask learning, sketch some recent developments, and demonstrate their applications in Computational Biology and Biological Imaging.
dc.identifier.pissn1610-1987
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/11390
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 28, No. 1
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectMultitask learning
dc.subjectRegularized risk minimization
dc.subjectTransfer learning
dc.titleRegularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging
dc.typeText/Journal Article
gi.citation.endPage33
gi.citation.startPage29

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Künstliche Intelligenz 28(1) - März 2014