On the versatility and polyvalence of certain statistical learning machines

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dc.contributor.authorFokoué, Ernest
dc.coverage.issue2cs
dc.coverage.volume8cs
dc.date.accessioned2020-05-05T06:21:04Z
dc.date.available2020-05-05T06:21:04Z
dc.date.issued2019cs
dc.description.abstractAs data science and its flurry of lucrative career opportunities continue to dominatestrategic planning meetings at companies and universities around the world, it isremarkable to notice that mathematics, the queen of all sciences, is still called uponto play a central role. I use mathematics here in senso lato to mean mathematicalsciences in general, including algebra, analysis, probability, statistics and theoret-ical computer science. Indeed all the statistical learning machines and traditionalstatistical methods permeating the articles of this special issue have in common thefact they all rest on strong mathematical foundations, even though some of the vastmathematical details are not shown here in some cases due to space constraints.en
dc.formattextcs
dc.format.extent97-99cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMathematics for Applications. 2019 vol. 8, č. 2, s. 97-99. ISSN 1805-3629cs
dc.identifier.issn1805-3629
dc.identifier.urihttp://hdl.handle.net/11012/186966
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.relation.ispartofMathematics for Applicationsen
dc.relation.urihttp://ma.fme.vutbr.cz/archiv/8_2/ma_8_2_0_editorial.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.rights.accessopenAccessen
dc.titleOn the versatility and polyvalence of certain statistical learning machinesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentÚstav matematikycs
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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