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undergraduate thesis
Rješavanje klasifikacijskih problema evolucijom stabala odluke
2016. urn:nbn:hr:168:658881
Kolobara, Vinko
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Kolobara, V. (2016). Rješavanje klasifikacijskih problema evolucijom stabala odluke (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:658881

Kolobara, Vinko. "Rješavanje klasifikacijskih problema evolucijom stabala odluke." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. https://urn.nsk.hr/urn:nbn:hr:168:658881

Kolobara, Vinko. "Rješavanje klasifikacijskih problema evolucijom stabala odluke." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. https://urn.nsk.hr/urn:nbn:hr:168:658881

Kolobara, V. (2016). 'Rješavanje klasifikacijskih problema evolucijom stabala odluke', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:658881

Kolobara V. Rješavanje klasifikacijskih problema evolucijom stabala odluke [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2016 [cited 2024 April 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:658881

V. Kolobara, "Rješavanje klasifikacijskih problema evolucijom stabala odluke", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2016. Available at: https://urn.nsk.hr/urn:nbn:hr:168:658881

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Metadata
TitleRješavanje klasifikacijskih problema evolucijom stabala odluke
Title (english)Classification using evolution of decision trees
AuthorVinko Kolobara
MentorDomagoj Jakobović (mentor)
Committee memberDomagoj Jakobović (predsjednik povjerenstva)
Committee memberNikolina Frid (član povjerenstva)
Committee memberMarko Đurasević (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2016-07-11, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Strojno učenje obuhvaća moćne metode koje se bave analizom podataka i učenjem računala na način sličan ljudskom. U ovome radu se primjenjuje genetsko programiranje na stabla odluke kako bi se riješili klasifikacijski problemi. I genetsko programiranje i stabla odluke su efikasni algoritmi u rješavanju tih problema, i kombinacijom se mogu dobiti još bolja svojstva. Za implementaciju je korišten ECF i implementiran je algoritam koji koristi jednostavno genetsko programiranje i isproban je na nekoliko skupova podataka na kojima su rezultati uglavnom dobri.
Abstract (english)
Machine learning consists of powerful methods which are dealing with data analysis and teaching the computer in a similar way as a human. In this thesis genetic programming is applied on decision trees to solve classification problems. Genetic programming and decision trees are efficient algorithms in solving those problems, and combining them even better properties are possible. ECF is used for implementation and the implemented algorithm uses simple genetic programming which tested on several data sets, shows some mostly good results.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:658881
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2018-02-06 09:43:39