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Title:ORODJA ZA STROJNO UČENJE - KLASIFIKACIJA
Authors:ID Zacirkovnik, Andreja (Author)
ID Brumen, Boštjan (Mentor) More about this mentor... New window
Files:.pdf VS_Zacirkovnik_Andreja_2013.pdf (2,58 MB)
MD5: 4A8AB7936965185FE427C277BBED22D9
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Diplomsko delo vsebuje teoretično osnovo strojnega učenja, podrobnejši opis podatkovnega rudarjenja ter njegove metode, natančneje klasifikacije. V drugem, praktičnem delu smo opisali tri prosto dostopna orodja, ki podpirajo strojno učenje ter metodo klasifikacije. Orodja smo testirali ter izmerili njihovo natančnost klasifikacije s statistično metodo cross-validation oz. prečnim preverjanjem klasifikacijske točnosti. Obravnavana in analizirana orodja, ki podpirajo metode strojnega učenja, so bila Weka, See5 in GATree. Rezultati analize so pokazali, da je od le teh najbolj natančno orodje za izvajanje klasifikacije program Weka.
Keywords:Strojno učenje, podatkovno rudarjenje, klasifikacija, orodja, Cross-validation.
Place of publishing:Maribor
Publisher:[A. Zacirkovnik]
Year of publishing:2013
PID:20.500.12556/DKUM-42095 New window
UDC:004.4'24:004.85(043.2)
COBISS.SI-ID:17504022 New window
NUK URN:URN:SI:UM:DK:PLH73KIF
Publication date in DKUM:19.09.2013
Views:4786
Downloads:923
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:TOOLS FOR MACHINE LEARNING - CLASSIFICATION
Abstract:Diploma paper contains theoretical basis of machine learning, a more detailed description of the data mining and its methods, specifically classification. In the second, practical part, we describe three tools that are available free and which support the machine learning and classification method. Tools were tested and measured the accuracy of the classification by the statistical method and cross-validation or cross-checking the classification accuracy. Discussed and analyzed tools that support the machine learning methods have been Weka, See5 and GATree. Results of the analysis showed that among these tools, the program Weka is the most accurate for the implementation of the classification.
Keywords:Machine learning, Data mining, classification, tools, cross-validation.


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