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undergraduate thesis
Algoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka
2015. urn:nbn:hr:200:212400
Radoš, Marin
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Programming Languages and Systems

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Radoš, M. (2015). Algoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka (Undergraduate thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:212400

Radoš, Marin. "Algoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2015. https://urn.nsk.hr/urn:nbn:hr:200:212400

Radoš, Marin. "Algoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2015. https://urn.nsk.hr/urn:nbn:hr:200:212400

Radoš, M. (2015). 'Algoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 30 May 2021, https://urn.nsk.hr/urn:nbn:hr:200:212400

Radoš M. Algoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2015 [cited 2021 May 30] Available at: https://urn.nsk.hr/urn:nbn:hr:200:212400

M. Radoš, "Algoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:200:212400

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Metadata
TitleAlgoritam k-najbližih susjeda i njegove unaprijeđene inačice za prepoznavanje uzoraka
Title (english)K-nearaest neighbors algorithm and its improved versions for pattern recognition
AuthorMarin Radoš
MentorGoran Martinović (mentor)
Committee memberGoran Martinović (predsjednik povjerenstva)
Committee memberHrvoje Glavaš (član povjerenstva)
Committee memberTomislav Keser (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Programming Languages and Systems)
Osijek
Defense date and country2015-09-24, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Program Engineering
Abstract
U ovom radu prikazan je problem klasifikacije uzoraka pomoću algoritma k-najbližih susjeda, te unaprjeđenje tog algoritma raznim načinima. Algoritam k-najbližih susjeda uspoređen je s njegovim unaprijeđenim inačicama na temelju eksperimentalnih rezultata dobivenih mjerenjem točnosti klasifikacije u različitim uvjetima, s različitim skupovima podataka i brojem susjeda k. Određeni su najveći nedostatci standardnog algoritma k-najbližih susjeda kroz funkciju udaljenosti, dodjeljivanje jednake važnosti svim atributima, te raspon veličina atributa. Ti nedostatci su riješeni u obliku unaprjeđenja algoritma drugačijom funkcijom udaljenosti, u ovom radu Manhattan, dodavanjem težine važnijim atributima, te normalizacijom skupa podataka. Kroz sva tri unaprjeđenja vidljivo je povećanje točnosti klasifikacije kroz dobivene eksperimentalne rezultate.
Abstract (english)
In this paper, the problem of classification was shown using the k-nearest neighbors algorithm and its improvements through various ways. K-nearest neighbors algorithm was compared with its improved variants based on the experimental results obtained by measuring classification accuracy in different conditions, with different data sets and the number of neighbors. Biggest disadvantages of the standard k-nearest neighbors algorithm were defined through a distance function, assigning equal importance to all the attributes, and size range of the attributes. These shortcomings are addressed in the form of improving the algorithm with different distance function, in this paper, Manhattan, adding weight to important attributes, and normalization of the data set. Through all three improvements, an increase in the classification accuracy is shown through the experimental results
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:212400
Study programmeTitle: Undergraduate University Study Programme in Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2016-07-06 11:00:33