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master's thesis
Robusne metode optimizacije u strojnom učenju
Zagreb: University of Zagreb, Faculty of Science, 2023. urn:nbn:hr:217:384663
Čuklić, Martina
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Čuklić, M. (2023). Robusne metode optimizacije u strojnom učenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:384663

Čuklić, Martina. "Robusne metode optimizacije u strojnom učenju." Master's thesis, University of Zagreb, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:217:384663

Čuklić, Martina. "Robusne metode optimizacije u strojnom učenju." Master's thesis, University of Zagreb, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:217:384663

Čuklić, M. (2023). 'Robusne metode optimizacije u strojnom učenju', Master's thesis, University of Zagreb, Faculty of Science, accessed 26 November 2024, https://urn.nsk.hr/urn:nbn:hr:217:384663

Čuklić M. Robusne metode optimizacije u strojnom učenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2023 [cited 2024 November 26] Available at: https://urn.nsk.hr/urn:nbn:hr:217:384663

M. Čuklić, "Robusne metode optimizacije u strojnom učenju", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:217:384663

Metadata
TitleRobusne metode optimizacije u strojnom učenju
Title (english)Robust optimization methods in machine learning
AuthorMartina Čuklić
MentorMarko Vrdoljak (mentor)
Committee memberMarko Vrdoljak (predsjednik povjerenstva)
Committee memberMarko Erceg (član povjerenstva)
Committee memberMatej Mihelčić (član povjerenstva)
Committee memberIvica Nakić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2023-03-02, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom radu prikazali smo primjene nekih tehnika robusne optimizacije u strojnom učenju. Robusna optimizacija važan je dio optimizacije koji se bavi nesigurnošću u podacima optimizacijskih problema. Kako se u današnje vrijeme strojno učenje često susreće s nesigurnošću, robusna optimizacija tu ima sve veću ulogu. Promatrana je veza između robusnosti i regulariziranog stroja potpornih vektora, točnije, pokazali smo da je standardni regularizirani stroj potpornih vektora posebni slučaj robusne optimizacije. Zatim je uvedena robusna regresija s pogreškom najmanjih kvadrata te je promatran slučaj gdje su značajke izložene smetnjama, to jest nesigurnosti. Osigurana je interpretacija Lasso algoritma s robusne perspektive, a na temelju te robusne interpretacije istražena su svojstva rijetkosti i konzistentnosti. Osim toga, predstavljen je rezultat koji nam govori da su rijetkost i stabilnost u kontradikciji, to jest da se ne mogu postići simultano.
Abstract (english)
In this paper, we presented some robust optimization techniques in machine learning. Robust optimization is an important part of optimization that deals with uncertainty in the data of optimization problems. As today’s machine learning is often faced with uncertainty, robust optimization plays an increasingly important role. The connection between robustness and regularized support vector machine was observed, more precisely, we showed that the standard regularized support vector machine is a special case of robust optimization. Robust regression with least squares error was introduced and the case where the features are exposed to disturbances, that is, uncertainties, was observed. An interpretation of the Lasso algorithm from a robust perspective is provided, and on the basis of this robust interpretation, the properties of sparsity and consistency are investigated. In addition, a result is presented that tells us that sparsity and stability are in contradiction, that is, they cannot be achieved simultaneously.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:384663
Study programmeTitle: Computer Science and Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra računarstva i matematike (magistar/magistra računarstva i matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-03-23 15:10:03