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Primena funkcionalnih normi za regularizaciju rangiranja nad temporalnim podacima

dc.contributor.advisorObradović, Zoran
dc.contributor.otherKovačević, Branko
dc.contributor.otherVučetić, Slobodan
dc.contributor.otherĐurović, Željko
dc.contributor.otherZhang, Kai
dc.creatorStojković, Ivan
dc.date.accessioned2019-01-18T09:36:24Z
dc.date.available2019-01-18T09:36:24Z
dc.date.available2020-07-03T08:36:28Z
dc.date.issued2018-05-11
dc.identifier.urihttp://nardus.mpn.gov.rs/handle/123456789/10624
dc.identifier.urihttp://eteze.bg.ac.rs/application/showtheses?thesesId=6477
dc.identifier.urihttps://fedorabg.bg.ac.rs/fedora/get/o:19210/bdef:Content/download
dc.identifier.urihttp://vbs.rs/scripts/cobiss?command=DISPLAY&base=70036&RID=50913039
dc.description.abstractQuantifying the properties of interest is an important problem in many domains, e.g., assessing the condition of a patient, estimating the risk of an investment or relevance of the search result. However, the properties of interest are often latent and hard to assess directly, making it dicult to obtain classication or regression labels, which are needed to learn a predictive models from observable features. In such cases, it is typically much easier to obtain relative comparison of two instances, i.e. to assess which one is more intense (with respect to the property of interest). One framework able to learn from such kind of supervised information is ranking SVM, and it will make a basis of our approach...sr
dc.description.abstractKvantikovanje osobina (karakteristika) od interesa je vazan problem u mnogim domenima, npr. utvrdivanje tezine bolesti kod pacijenata, ocena rizika investicije ili relevantnost vracenih rezultata pretrage. Medutim, osobine od interesa su cesto latentne i tesko se mogu izmeriti direktno, sto otezava dobijanje klasikacionih oznaka (labela) ili ciljeva za regresiju, koji su potrebni za ucenje prediktivnih modela iz merljivih karakteristika. U takvim slucajevima obicno je mnogo lakse pribaviti relativno poredenje dva slucaja, tj. proceniti koji od dva je intenzivniji (iz ugla karakteristike od interesa). Jedna klasa algoritama koji mogu uciti iz ovakvih informacija je SVM za rangiranje i on ce biti osnova ovde predlozenog pristupa...en
dc.formatapplication/pdf
dc.languageen
dc.publisherУниверзитет у Београду, Електротехнички факултетsr
dc.rightsopenAccessen
dc.sourceУниверзитет у Београдуsr
dc.subjectSVM rankingsr
dc.subjectSVM rangiranjeen
dc.subjectscoring function learningsr
dc.subjectfunctional norm regularizationsr
dc.subjectproximal algorithms for optimizationsr
dc.subjecttemporal datasr
dc.subjectucenje funkcija za bodovanjeen
dc.subjectfunkcionalna regularizacija normamaen
dc.subjectproksimalni algoritmi za optimizacijuen
dc.subjecttemporalni podacien
dc.titleFunctional norm regularization for margin-based ranking on temporal datasr
dc.title.alternativePrimena funkcionalnih normi za regularizaciju rangiranja nad temporalnim podacimaen
dc.typedoctoralThesis
dc.rights.licenseBY-NC-SA
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/5942/IzvestajKomisije18792.pdf
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/5941/Disertacija.pdf


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