dc.contributor.author | Matias, Alan L. S. | |
dc.contributor.author | Mattos, César L. C. | |
dc.contributor.author | Kärkkäinen, Tommi | |
dc.contributor.author | Gomes, João P. P. | |
dc.contributor.author | Rocha Neto, Ajalmar R. da | |
dc.contributor.editor | Rojas, Ignacio | |
dc.contributor.editor | Joya, Gonzalo | |
dc.contributor.editor | Catala, Andreu | |
dc.date.accessioned | 2019-10-17T09:17:44Z | |
dc.date.available | 2019-10-17T09:17:44Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Matias, A. L. S., Mattos, C. L. C., Kärkkäinen, T., Gomes, J. P.P., & Rocha Neto, A. R. D. (2019). OnMLM : An Online Formulation for the Minimal Learning Machine. In I. Rojas, G. Joya, & A. Catala (Eds.), <i>IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part I</i> (pp. 557-568). Springer. Lecture Notes in Computer Science, 11506. <a href="https://doi.org/10.1007/978-3-030-20521-8_46" target="_blank">https://doi.org/10.1007/978-3-030-20521-8_46</a> | |
dc.identifier.other | CONVID_32145299 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/65933 | |
dc.description.abstract | Minimal Learning Machine (MLM) is a nonlinear learning algorithm designed to work on both classification and regression tasks. In its original formulation, MLM builds a linear mapping between distance matrices in the input and output spaces using the Ordinary Least Squares (OLS) algorithm. Although the OLS algorithm is a very efficient choice, when it comes to applications in big data and streams of data, online learning is more scalable and thus applicable. In that regard, our objective of this work is to propose an online version of the MLM. The Online Minimal Learning Machine (OnMLM), a new MLM-based formulation capable of online and incremental learning. The achievements of OnMLM in our experiments, in both classification and regression scenarios, indicate its feasibility for applications that require an online learning framework. | en |
dc.format.extent | 940 | |
dc.format.mimetype | application/pdf | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartof | IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part I | |
dc.relation.ispartofseries | Lecture Notes in Computer Science | |
dc.rights | In Copyright | |
dc.subject.other | online learning | |
dc.subject.other | incremental learning | |
dc.subject.other | stochastic optimization | |
dc.subject.other | Minimal Learning Machine | |
dc.title | OnMLM : An Online Formulation for the Minimal Learning Machine | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-201910174506 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.relation.isbn | 978-3-030-20520-1 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 557-568 | |
dc.relation.issn | 0302-9743 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © Springer International Publishing AG 2019 | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | International Work-Conference on Artificial Neural Networks | |
dc.relation.grantnumber | 315550 | |
dc.relation.grantnumber | 311877 | |
dc.subject.yso | stokastiset prosessit | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | big data | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p11400 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p27202 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.1007/978-3-030-20521-8_46 | |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Suomen Akatemia | fi |
dc.relation.funder | Suomen Akatemia | fi |
jyx.fundingprogram | Academy Programme, AoF | en |
jyx.fundingprogram | Research profiles, AoF | en |
jyx.fundingprogram | Akatemiaohjelma, SA | fi |
jyx.fundingprogram | Profilointi, SA | fi |
jyx.fundinginformation | The work was supported by the Academy of Finland from grants 311877 and 315550. | |
dc.type.okm | A4 | |