Research output: Contribution to journal › Article › peer-review
Group approach to solving the tasks of recognition. / Amirgaliyev, Yedilkhan; Berikov, Vladimir; Cherikbayeva, Lyailya S. et al.
In: Yugoslav Journal of Operations Research, Vol. 29, No. 2, 01.01.2019, p. 177-192.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Group approach to solving the tasks of recognition
AU - Amirgaliyev, Yedilkhan
AU - Berikov, Vladimir
AU - Cherikbayeva, Lyailya S.
AU - Latuta, Konstantin
AU - Bekturgan, Kalybekuuly
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. A probabilistic model of classification with the use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.
AB - In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. A probabilistic model of classification with the use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.
KW - Classification
KW - Hyper Spectral Image
KW - Recognition
KW - Semi-Supervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85068467475&partnerID=8YFLogxK
UR - https://elibrary.ru/item.asp?id=41667201
U2 - 10.2298/YJOR180822032Y
DO - 10.2298/YJOR180822032Y
M3 - Article
AN - SCOPUS:85068467475
VL - 29
SP - 177
EP - 192
JO - Yugoslav Journal of Operations Research
JF - Yugoslav Journal of Operations Research
SN - 0354-0243
IS - 2
ER -
ID: 20780983