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dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2019-02-11T11:17:52Z
dc.date.available2019-02-11T11:17:52Z
dc.date.issued2018fi
dc.identifier.citationKärkkäinen, T. (2018). Extreme Minimal Learning Machine. In <em>ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</em> (pp. 237-242). ESANN. Retrieved from <a href="https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-72.pdf">https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-72.pdf</a>fi
dc.identifier.otherTUTKAID_80471
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/62746
dc.description.abstractExtreme Learning Machine (ELM) and Minimal Learning Machine (MLM) are nonlinear and scalable machine learning techniques with randomly generated basis. Both techniques share a step where a matrix of weights for the linear combination of the basis is recovered. In MLM, the kernel in this step corresponds to distance calculations between the training data and a set of reference points, whereas in ELM transformation with a sigmoidal activation function is most commonly used. MLM then needs additional interpolation step to estimate the actual distance-regression based output. A natural combination of these two techniques is proposed here, i.e., to use a distance-based kernel characteristic in MLM in ELM. The experimental results show promising potential of the proposed technique.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherESANN
dc.relation.ispartofESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.urihttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-72.pdf
dc.rightsIn Copyright
dc.subject.otherkoneoppiminenfi
dc.subject.othermachine learningfi
dc.subject.otherExtreme Learning Machinefi
dc.subject.otherMinimal Learning Machinefi
dc.titleExtreme Minimal Learning Machinefi
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201901281316
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-01-28T07:15:14Z
dc.relation.isbn978-287587047-6
dc.description.reviewstatuspeerReviewed
dc.format.pagerange237-242
dc.type.versionPublisher's PDF
dc.rights.copyright© Author, 2018
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en


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