File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Localized multiple kernel learning|A convex approach

TitleLocalized multiple kernel learning|A convex approach
Authors
KeywordsGeneralization analysis
Localized algorithms
Multiple kernel learning
Issue Date2016
Citation
Journal of Machine Learning Research, 2016, v. 63, p. 81-96 How to Cite?
AbstractWe propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain general- ization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of com- putational biology and computer vision show that convex localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.
Persistent Identifierhttp://hdl.handle.net/10722/329541
ISSN
2023 Impact Factor: 4.3
2023 SCImago Journal Rankings: 2.796

 

DC FieldValueLanguage
dc.contributor.authorLei, Yunwen-
dc.contributor.authorBinder, Alexander-
dc.contributor.authorDogan, Urun-
dc.contributor.authorKloft, Marius-
dc.date.accessioned2023-08-09T03:33:32Z-
dc.date.available2023-08-09T03:33:32Z-
dc.date.issued2016-
dc.identifier.citationJournal of Machine Learning Research, 2016, v. 63, p. 81-96-
dc.identifier.issn1532-4435-
dc.identifier.urihttp://hdl.handle.net/10722/329541-
dc.description.abstractWe propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain general- ization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of com- putational biology and computer vision show that convex localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.-
dc.languageeng-
dc.relation.ispartofJournal of Machine Learning Research-
dc.subjectGeneralization analysis-
dc.subjectLocalized algorithms-
dc.subjectMultiple kernel learning-
dc.titleLocalized multiple kernel learning|A convex approach-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-85059295887-
dc.identifier.volume63-
dc.identifier.spage81-
dc.identifier.epage96-
dc.identifier.eissn1533-7928-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats