Title
Functional norm regularization for margin-based ranking on temporal data
Creator
Stojković, Ivan, 1987-
Copyright date
2018
Object Links
Select license
Autorstvo-Nekomercijalno-Deliti pod istim uslovima 3.0 Srbija (CC BY-NC-SA 3.0)
License description
Dozvoljavate umnožavanje, distribuciju i javno saopštavanje dela, i prerade, ako se navede ime autora na način odredjen od strane autora ili davaoca licence i ako se prerada distribuira pod istom ili sličnom licencom. Ova licenca ne dozvoljava komercijalnu upotrebu dela i prerada. Osnovni opis Licence: http://creativecommons.org/licenses/by-nc-sa/3.0/rs/deed.sr_LATN Sadržaj ugovora u celini: http://creativecommons.org/licenses/by-nc-sa/3.0/rs/legalcode.sr-Latn
Language
English
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 11.05.2018.
Other responsibilities
mentor
Obradović, Zoran
mentor
Kovačević, Branko, 1951-
član komisije
Vučetić, Slobodan
član komisije
Đurović, Željko, 1964-
član komisije
Zhang, Kai
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Beogradu
Faculty
Elektrotehnički fakultet
Alternative title
Primena funkcionalnih normi za regularizaciju rangiranja nad temporalnim podacima
Publisher
[I. Stojković]
Format
XIX, 74 listova
description
Electrical Engineering and Computer Sciences - Data analysis and machine learning / Elektrotehnika i Racunarske nauke - Analiza podataka i masinsko ucenje
Abstract (en)
Quantifying 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...
Abstract (sr)
Kvantikovanje 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...
Authors Key words
SVM ranking, scoring function learning, functional norm
regularization, proximal algorithms for optimization, temporal data
Authors Key words
SVM rangiranje, ucenje funkcija za bodovanje, funkcionalna
regularizacija normama, proksimalni algoritmi za optimizaciju, temporalni podaci
Type
Tekst
Abstract (en)
Quantifying 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...
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