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dc.contributor.authorHämäläinen, Joonas
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2020-11-04T08:49:25Z
dc.date.available2020-11-04T08:49:25Z
dc.date.issued2020
dc.identifier.citationHämäläinen, J., & Kärkkäinen, T. (2020). Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression. In <i>ESANN 2020 : Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</i> (pp. 691-696). ESANN. <a href="https://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdf" target="_blank">https://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdf</a>
dc.identifier.otherCONVID_43492634
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72475
dc.description.abstractMulti-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM.en
dc.format.extent726
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherESANN
dc.relation.ispartofESANN 2020 : Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.urihttps://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdf
dc.rightsIn Copyright
dc.subject.otherMulti-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods
dc.subject.otherthe minimal learning machine (MLM) and the extreme minimal learning machine (EMLM)
dc.subject.otherin problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential
dc.subject.otheremphasizing the utility of the problem transformation especially with the EMLM.
dc.titleProblem Transformation Methods with Distance-Based Learning for Multi-Target Regression
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202011046511
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-2-87587-074-2
dc.description.reviewstatuspeerReviewed
dc.format.pagerange691-696
dc.type.versionpublishedVersion
dc.rights.copyright© The Authors, 2019
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.grantnumber311877
dc.relation.grantnumber315550
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramAcademy Programme, AoFen


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