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Title:Metode za ovrednotenje algoritmov strojnega učenja
Authors:ID Flisar, Iva (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
ID Karakatič, Sašo (Comentor)
Files:.pdf UN_Flisar_Iva_2016.pdf (3,31 MB)
MD5: 124EC2DDF6875B46CCEB14C16CC346D2
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Strojno učenje je pojem, tesno povezan s podatkovnim rudarjenjem, saj s pomočjo učnih algoritmov iščemo vzorce v podatkih. V diplomskem delu smo predstavili in opisali različne učne algoritme, ki se uporabljajo v procesu podatkovnega rudarjenja. Naš glavni cilj je bila predstavitev različnih metrik ovrednotenja učnih algoritmov. V ta namen smo v praktičnem delu diplomske naloge z različnimi metrikami ovrednotili učne algoritme. Eksperiment ovrednotenja smo izvedli na različnih podatkovnih množicah, ki smo jih razdelili z dvema različnima tipoma razdelitve – navzkrižno validacijo ter z metodo razdelitve.
Keywords:strojno učenje, klasifikacija, učni algoritmi, ovrednotenje algoritmov, metrike ocenjevanja
Place of publishing:[Maribor
Publisher:I. Flisar
Year of publishing:2016
PID:20.500.12556/DKUM-62019 New window
UDC:004.8.021:004.6(043.2)
COBISS.SI-ID:20177942 New window
NUK URN:URN:SI:UM:DK:118TBKP1
Publication date in DKUM:06.09.2016
Views:2185
Downloads:290
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Methods for the evaluation of machine learning algorithms
Abstract:Machine learning is a concept closely related to data mining; we are looking for patterns in data, by using learning algorithms. In our thesis, we presented and described various learning algorithms that are used for data mining. Our main objective was to present different evaluation metrics of learning algorithms. We evaluated the learning algorithms in the practical part of the thesis. The experiment of evaluation was performed on different datasets which were divided with two different dividing methods - the cross validation and the holdout method. 
Keywords:machine learning, classification, learning algorithms, evalutation algorithms, evaluation metrics


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