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Title:Primerjalna analiza Evklidske in Poincaréjeve metrike v algoritmih strojnega učenja : magistrska naloga
Authors:ID Trpin, Alenka (Author)
ID Boshkoska, Biljana Mileva (Mentor) More about this mentor... New window
Files:.pdf MAG_2018_Alenka_Trpin.pdf (1,07 MB)
MD5: DBA730C74000F9A0A112741087B073CD
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FIŠ - Faculty of Information Studies in Novo mesto
Abstract:Živimo v času, ko si življenja brez računalnikov ne predstavljamo. Množična uporaba tako imenovane informacijsko komunikacijske tehnologije je proizvedla velike količine podatkov, ki jih sami ne moremo interpretirati in uporabiti. Z orodji podatkovnega rudarjenja in strojnega učenja se velike množice podatkov lahko obdelajo in uporabijo za napovedovanje in klasifikacijo. Eno od orodij za tako obdelavo podatkov je WEKA. Naloga temelji na osnovnem klasifikacijskem agoritem k najbližjih sosedov. V različnih panogah (gospodarstvo, zdravstvo, vojska...) se vedno bolj uporablja in shranjuje podatkovne baze raznovrstnih slik oziroma fotografij. Pri prepoznavanju podobosti med dvema fotografijama je pomembno, da algoritem prepozna določene vzorce. Prepoznavanje temelji na metriki. V ta namen je v orodje WEKA implementiran algoritem, ki temelji na Poincaréjevi metriki. Testiran je na podatkovni množici fotografij. Za namen primerjave je bil uporabljen algoritmom, ki temelji na evklidski metriki.
Keywords:podatkovno rudarjenje, strojno učenje, Poincaréjeva metrika, WEKA, k najbližjih sosedov, segmentacija
Place of publishing:Novo mesto
Place of performance:Novo mesto
Publisher:[A. Trpin]
Year of publishing:2018
Year of performance:2018
Number of pages:IX, 67 str.
PID:20.500.12556/ReVIS-5355 New window
COBISS.SI-ID:2048549907 New window
UDC:004.85:004.421(043.2)
Note:Na ov.: Magistrska naloga : študijskega programa druge stopnje;
Publication date in ReVIS:30.11.2018
Views:5270
Downloads:158
Metadata:XML DC-XML DC-RDF
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TRPIN, Alenka, 2018, Primerjalna analiza Evklidske in Poincaréjeve metrike v algoritmih strojnega učenja : magistrska naloga [online]. Master’s thesis. Novo mesto : A. Trpin. [Accessed 30 March 2025]. Retrieved from: https://revis.openscience.si/IzpisGradiva.php?lang=eng&id=5355
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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:30.11.2018

Secondary language

Language:English
Abstract:Today we cannot imagine life without computers. The massive use of the information communication technologies has produced large amounts of data that are difficult to interpret and use. With data mining tools and machine learning methods, large data sets can be processed and used for prediction and classification. One of the tools for such data processing is WEKA. The research in this thesis focuses on the basic classification algorithm the k nearest neighbors. In different industries (economy, health, military...) it increasingly uses and stores databases of various images or photographs. When recognizing the similarity between two photographs, it is important that the algorithm recognizes certain patterns. Recognition is based on metrics. For this purposes an algorithm based on Poincaré metric is implemented in WEKA and tested on a data set of photos. A comparison was made on algorithm based on Euclidean metric.
Keywords:data mining, machine learning, Poincaré metric, WEKA, k nearest neighbours, segmentation


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