prikaz prve stranice dokumenta Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala
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master's thesis
Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:255073
Josipović, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Josipović, M. (2021). Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:255073

Josipović, Marko. "Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:255073

Josipović, Marko. "Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:255073

Josipović, M. (2021). 'Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 October 2024, https://urn.nsk.hr/urn:nbn:hr:168:255073

Josipović M. Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 October 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:255073

M. Josipović, "Algoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:255073

Metadata
TitleAlgoritmi strojnog učenja za predviđanje gledanosti televizijskih kanala
Title (english)Machine Learning Algorithms for Forecasting Television Channels Viewership
AuthorMarko Josipović
MentorAlan Jović (mentor)
Committee memberAlan Jović (predsjednik povjerenstva)
Committee memberJan Šnajder (član povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-09-17, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad opisuje teorijsku podlogu algoritama strojnog učenja koji su razmatrani za predviđanje gledanosti televizijskih kanala. Eksperimentalna usporedba odabranih algoritama pokazala je da algoritam stroja potpornih vektora za regresiju (SVR) daje najbolje rezultate predviđanja dostupnih podataka o stvarnim gledanostima. Algoritam linearne regresije ne daje zadovoljavajuće rezultate, a algoritam slučajne šume (ExtraTrees) ima malo lošije rezultate od stroja potpornih vektora. Opisana je i implementacija web aplikacije koja korisnicima omogućava predviđanje gledanosti na temelju učitanog rasporeda emitiranja. Aplikacija se može koristiti kao alat za optimizaciju rasporeda. Zbog velike vremenske složenosti algoritma stroja potpornih vektora, za korištenje u aplikaciji odabran je algoritam slučajne šume.
Abstract (english)
This paper describes the theoretical basis of machine learning algorithms that have been considered for forecasting the viewership of television channels. An experimental comparison of chosen algorithms showed that the support vector regression algorithm (SVR) gives the best results on the available real world viewership data. The linear regression algorithm does not give satisfactory results, and the extremely randomized trees algorithm (ExtraTrees) has slightly worse results than the support vector machine algorithm. The implementation of a web application that allows users to forecast viewership based on the uploaded broadcast schedule is also described. The application can be used as a schedule optimization tool. Due to the high time complexity of the support vector machine algorithm, the randomized trees algorithm was chosen for use in the application.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:255073
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 12:32:24