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Title:SAMODEJNO PRIMERJANJE METOD STROJNEGA UČENJA V OKOLJU WEKA
Authors:ID Špindler, Matic (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf UNI_Spindler_Matic_2013.pdf (1,89 MB)
MD5: 92F008FA8D3459F51B9DA8B1678B4A97
 
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 je opisano strojno učenje in problemi, ki jih rešujemo s strojnim učenjem. Zajeto in opisano je tudi okolje Weka, s pomočjo katerega lahko iz podatkovnih množic pridobivamo skrito znanje. Predstavljena je tudi struktura ARFF datoteke, kot tudi princip ustvarjanja takšne datoteke ter uporaba le-te. Opisan je razvoj programa za samodejno primerjavo metod strojnega učenja, ki kot rezultat vrne urejen izpis podatkov testiranj podatkovnih množic v Excel datoteko. Princip razvoja programa obsega še opis uporabljene programske opreme in tehnologij, izdelavo uporabniškega vmesnika programa ter nekatere primere kode programskega jezika Java kot rešitev zastavljenega problema.
Keywords:strojno učenje, okolje Weka, ARFF datoteke, podatkovne množice
Place of publishing:Maribor
Publisher:[M. Špindler]
Year of publishing:2013
PID:20.500.12556/DKUM-42070 New window
UDC:004.5:004.6(043.2)
COBISS.SI-ID:17462294 New window
NUK URN:URN:SI:UM:DK:ZFZ0EOIT
Publication date in DKUM:18.09.2013
Views:2330
Downloads:196
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:AUTOMATIC COMPARISON OF MACHINE LEARNING METHODS IN WEKA
Abstract:This bachelor degree describes machine learning, problems that are solved with methods of it and Weka environment with which we can search for hidden knowledge in datasets. It also includes description and structure of ARFF file, principles of creating it and using it as a dataset. Bachelor degree also includes development of program for automatic comparison of machine learning methods and process of showing results, from tested datasets, in output Excel file. Software and technologies used in project, creation of user interface and samples of Java codes as the solution of problem are also presented.
Keywords:machine learning, Weka environment, ARFF files, datasets


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