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Title:
INDUKTIVNO UČENJE IZ OPAZOVANJ
Authors:
ID
Pišorn, Miha
(Author)
ID
Guid, Nikola
(Mentor)
More about this mentor...
ID
Strnad, Damjan
(Comentor)
Files:
UNI_Pisorn_Miha_2014.pdf
(2,45 MB)
MD5: B2AE36062A0ACC477ED3D685E7A7C332
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu predstavimo učenje iz podatkov, kot model predvidevanja uporabimo odločitvena drevesa. Preučimo problem prekomernega prilagajanja in pogoste metode za njegovo omiljenje. Ansambelsko učenje je koncept v okviru umetne inteligence, ki združuje metode, ki sestavijo nabor klasifikatorjev in klasificirajo nove vhodne podatke na podlagi glasovanja. Te metode preučimo in pokažemo, zakaj se pogosto odrežejo bolje od posameznih klasifikatorjev. Implementiramo pogosto uporabljan algoritem Adaboost in preizkusimo njegovo obnašanje. Kot klasifikatorje uporabimo odločitvena drevesa.
Keywords:
umetna inteligenca
,
strojno učenje
,
odločitveno drevo
,
ansambelsko učenje
,
Adaboost
Place of publishing:
Maribor
Publisher:
[M. Pišorn]
Year of publishing:
2014
PID:
20.500.12556/DKUM-46375
UDC:
004.89(043.2)
COBISS.SI-ID:
18546710
NUK URN:
URN:SI:UM:DK:PSA7HASN
Publication date in DKUM:
06.03.2015
Views:
2845
Downloads:
166
Metadata:
Categories:
KTFMB - FERI
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Vancouver
:
PIŠORN, Miha, 2014,
INDUKTIVNO UČENJE IZ OPAZOVANJ
[online]. Bachelor’s thesis. Maribor : M. Pišorn. [Accessed 24 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=46375
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Secondary language
Language:
English
Title:
INDUCTIVE LEARNING FROM OBSERVATION
Abstract:
In this diploma thesis we review learning from data using decision trees as a prediction model. We study the problem of overfitting and review common methods used to contain it. Ensemble learning is a concept in artificial intelligence that encompasses methods constructing a set of classifiers and classify new input data by taking a vote of their predictions. We review these methods and show why they often outperform single classifiers. We implement commonly used Adaboost algorithm and test its behavior, using decision trees as classifiers.
Keywords:
artificial intelligence
,
machine learning
,
decision tree
,
ensemble learning
,
Adaboost
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