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master's thesis
Logistička regresija rijetkih ulaznih podataka
2019. urn:nbn:hr:126:356254
Dean, Renato
Josip Juraj Strossmayer University of Osijek
Department of Mathematics
Chair of Applied Mathematics and Computer Science
Computer Science and Machine Learning Research Group

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Dean, R. (2019). Logistička regresija rijetkih ulaznih podataka (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:356254

Dean, Renato. "Logistička regresija rijetkih ulaznih podataka." Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2019. https://urn.nsk.hr/urn:nbn:hr:126:356254

Dean, Renato. "Logistička regresija rijetkih ulaznih podataka." Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2019. https://urn.nsk.hr/urn:nbn:hr:126:356254

Dean, R. (2019). 'Logistička regresija rijetkih ulaznih podataka', Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, accessed 02 June 2023, https://urn.nsk.hr/urn:nbn:hr:126:356254

Dean R. Logistička regresija rijetkih ulaznih podataka [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics; 2019 [cited 2023 June 02] Available at: https://urn.nsk.hr/urn:nbn:hr:126:356254

R. Dean, "Logistička regresija rijetkih ulaznih podataka", Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, Osijek, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:126:356254

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Metadata
TitleLogistička regresija rijetkih ulaznih podataka
Title (english)Sparse Logistic Regression
AuthorRenato Dean
MentorSlobodan Jelić (mentor)
Committee memberKristian Sabo (predsjednik povjerenstva)
Committee memberSlobodan Jelić (član povjerenstva)
Committee memberDomagoj Matijević (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Department of Mathematics
(Chair of Applied Mathematics and Computer Science)
(Computer Science and Machine Learning Research Group)
Osijek
Defense date and country2019-12-06, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Mathematical Logic and Accounting
Abstract
U ovom radu će kroz primjere biti predstavljeni neki od problema strojnog učenja te klasične metode za njihovo rješavanje: linearna, logistička i softmax regresija. Objasnit ćemo matematičku pozadinu svake od metoda, upoznat ćemo se sa njihovim nedostatcima te ćemo predstaviti neke od načina korigiranja istih: redukciju broja svojstava i regularizaciju. Pokazat ćemo i primjenu logističke re- gresije kao modela za klasikaciju slika iz baze MRNet u prakticnom dijelu rada koji je izrađen u programskom jeziku Python.
Abstract (english)
In this paper we will present some of the examples of machine learning problems and the classical methods used for their solving: linear, logistic and somax regression. We will explain the mathematical background of each of the methods, familiarize ourselves with their shortcomings and present some ways of correcting them, such as reduction of the number of features and regularization. We will also show how to use logistic regression as a classifying model in a practical project made in Python programming language.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:356254
Study programmeTitle: Mathematics; specializations in: Financial and Statistical Mathematics, Mathematics and Computer Science, Industrial and Applied Mathematics
Course: Financial and Statistical Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2020-12-07 07:26:40