Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
|
|
SLO
|
ENG
|
Cookies and privacy
DKUM
EPF - Faculty of Business and Economics
FE - Faculty of Energy Technology
FERI - Faculty of Electrical Engineering and Computer Science
FF - Faculty of Arts
FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
FKBV - Faculty of Agriculture and Life Sciences
FKKT - Faculty of Chemistry and Chemical Engineering
FL - Faculty of Logistic
FNM - Faculty of Natural Sciences and Mathematics
FOV - Faculty of Organizational Sciences in Kranj
FS - Faculty of Mechanical Engineering
FT - Faculty of Tourism
FVV - Faculty of Criminal Justice and Security
FZV - Faculty of Health Sciences
MF - Faculty of Medicine
PEF - Faculty of Education
PF - Faculty of Law
UKM - University of Maribor Library
UM - University of Maribor
UZUM - University of Maribor Press
COBISS
Faculty of Business and Economic, Maribor
Faculty of Agriculture and Life Sciences, Maribor
Faculty of Logistics, Celje, Krško
Faculty of Organizational Sciences, Kranj
Faculty of Criminal Justice and Security, Ljubljana
Faculty of Health Sciences
Library of Technical Faculties, Maribor
Faculty of Medicine, Maribor
Miklošič Library FPNM, Maribor
Faculty of Law, Maribor
University of Maribor Library
Bigger font
|
Smaller font
Introduction
Search
Browsing
Upload document
For students
For employees
Statistics
Login
First page
>
Show document
Show document
Title:
Celovit pregled orodij za samodejno strojno učenje : diplomsko delo
Authors:
ID
Milošič, Tomi
(Author)
ID
Fister, Iztok
(Mentor)
More about this mentor...
ID
Pečnik, Špela
(Comentor)
Files:
VS_Milosic_Tomi_2021.pdf
(1,37 MB)
MD5: 720B9013188E08F36F9CCC2212890F2F
PID:
20.500.12556/dkum/5b29bdb9-6d4d-4d1d-9fd3-af9b77757ad2
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomski nalogi smo raziskali področje samodejnega strojnega učenja, osredotočili smo se na orodja za samodejno strojno učenje in poudarili njihove prednosti in slabosti na podlagi primerjave glede na različne nabore podatkov. Osredotočili smo se tudi na metodo klasifikacije, saj je to pogosta naloga strojnega učenja. Namen diplomske naloge je ugotoviti, katero orodje je najbolj optimalno za posamezno nalogo. Diplomsko nalogo smo razdelili na dva dela, in sicer teoretični del in praktični del. V teoretičnem delu smo se osredotočili na razjasnitev pojmov, zgodovino strojnega učenja in opis orodij samodejnega strojnega učenja. V praktičnem delu smo opravili primerjave med orodji in ugotovili, da le-ta vračajo podobne rezultate klasifikacije različno hitro. Ugotovili smo tudi, da so orodja namenjena uporabnikom, ki niso strokovnjaki na področju strojnega učenja, in da si orodja delijo skupne značilnosti.
Keywords:
klasifikacija
,
strojno učenje
,
samodejno strojno učenje
,
umetna inteligenca
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[T. Milošič]
Year of publishing:
2021
Number of pages:
XII, 52 str.
PID:
20.500.12556/DKUM-80067
UDC:
004.85(043.2)
COBISS.SI-ID:
94366979
Publication date in DKUM:
18.10.2021
Views:
1444
Downloads:
244
Metadata:
Categories:
KTFMB - FERI
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
MILOŠIČ, Tomi, 2021,
Celovit pregled orodij za samodejno strojno učenje : diplomsko delo
[online]. Bachelor’s thesis. Maribor : T. Milošič. [Accessed 29 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=80067
Copy citation
Average score:
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
(0 votes)
Your score:
Voting is allowed only for
logged in
users.
Share:
Similar works from our repository:
Sequential afatinib and osimertinib in patients with EGFR mutation-positive non-small-cell lung cancer
Sequential treatment with afatinib and osimertinib in patients with EGFR mutation-positive non-small-cell lung cancer
Selpercatinib in RET fusion-positive non-small-cell lung cancer (SIREN)
Trans-esophageal endobronchial ultrasound-guided needle aspiration (EUS-B-NA)
Access to novel drugs for non-small cell lung cancer in Central and Southeastern Europe
Similar works from other repositories:
Vpliv izražanja receptorja za inzulinu podoben rastni dejavnik 1 (IGF1R) na preživetje pri razsejanem nedrobnoceličnem raku pljuč
Gene expression levels of the prolyl hydroxylase domain proteins PHD1 and PHD2 but not PHD3 are decreased in primary tumours and correlate with poor prognosis of patients with surgically resected non-small-cell lung cancer
Uporabnost imunohistokemičnega določanja mutacij receptorja za epidermalni rastni faktor pri raku pljuč
Sistematični pregled opazovalnih raziskav učinkovitosti in varnosti zaviralcev imunskih kontrolnih točk pri zdravljenju razsejanega nedrobnoceličnega raka pljuč
Pomen tumorskih matičnih celic in epitelno-mezenhimskega prehoda pri napredovanju nedrobnoceličnega raka pljuč
Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.
Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
27.08.2021
Secondary language
Language:
English
Title:
A comprehensive overview of automated machine learning tools
Abstract:
In the thesis, we researched the field of automatic machine learning, focused on automatic machine learning tools, and highlighted their advantages and disadvantages based on comparison with different data sets. The focus was also on classification which is a common task of machine learning. The purpose of the thesis was to determine which tool is most optimal for individual tasks. The thesis was divided into two parts, a theoretical part, and an empirical part. In the theoretical part, the clarification of concepts, the history of machine learning, and the description of automatic machine learning tools was presented. In the empirical part, comparisons were made between tools and it was found that AutoML tools return similar classification results at different speeds. We also found that the tools are intended for users who are not experts in the field of machine learning and that the tools share common features.
Keywords:
classification
,
machine learning
,
automated machine learning
,
artificial intelligence
Comments
Leave comment
You must
log in
to leave a comment.
Comments (0)
0 - 0 / 0
There are no comments!
Back
