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dc.contributor.authorDias, Madson L. D.
dc.contributor.authorSousa, Lucas S.
dc.contributor.authorRocha Neto, Ajalmar R. da
dc.contributor.authorMattos, César L. C.
dc.contributor.authorGomes, João P. P.
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2019-11-12T13:39:29Z
dc.date.available2019-11-12T13:39:29Z
dc.date.issued2019
dc.identifier.citationDias, Madson L. D.; Sousa, Lucas S.; Rocha Neto, Ajalmar R. da; Mattos, César L. C.; Gomes, João P. P.; Kärkkäinen, Tommi (2019). Sparse minimal learning machine using a diversity measure minimization. In ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN, 269-274. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-178.pdf
dc.identifier.otherCONVID_32125250
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/66333
dc.description.abstractThe minimal learning machine (MLM) training procedure consists in solving a linear system with multiple measurement vectors (MMV) created between the geometric congurations of points in the input and output spaces. Such geometric congurations are built upon two matrices created using subsets of input and output points, named reference points (RPs). The present paper considers an extension of the focal underdetermined system solver (FOCUSS) for MMV linear systems problems with additive noise, named regularized MMV FOCUSS (regularized M-FOCUSS), and evaluates it in the task of selecting input reference points for regression settings. Experiments were carried out using UCI datasets, where the proposal was able to produce sparser models and achieve competitive performance when compared to the regular strategy of selecting MLM input RPs.en
dc.format.extent696
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.publisherESANN
dc.relation.ispartofESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.urihttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-178.pdf
dc.rightsIn Copyright
dc.titleSparse minimal learning machine using a diversity measure minimization
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201911124844
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-2-87587-065-0
dc.description.reviewstatuspeerReviewed
dc.format.pagerange269-274
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.fundingprogramMuut, SAfi
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundingprogramOthers, AoFen
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundinginformationThe authors would like to thank UFC, FUNCAP, and Academy of Finland (grants 311877 and 315550) for supporting their research.


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