Standard

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

Harvard

Amirgaliyev, Y, Berikov, V, Cherikbayeva, LS, Latuta, K & Bekturgan, K 2019, 'Group approach to solving the tasks of recognition', Yugoslav Journal of Operations Research, vol. 29, no. 2, pp. 177-192. https://doi.org/10.2298/YJOR180822032Y

APA

Amirgaliyev, Y., Berikov, V., Cherikbayeva, L. S., Latuta, K., & Bekturgan, K. (2019). Group approach to solving the tasks of recognition. Yugoslav Journal of Operations Research, 29(2), 177-192. https://doi.org/10.2298/YJOR180822032Y

Vancouver

Amirgaliyev Y, Berikov V, Cherikbayeva LS, Latuta K, Bekturgan K. Group approach to solving the tasks of recognition. Yugoslav Journal of Operations Research. 2019 Jan 1;29(2):177-192. doi: 10.2298/YJOR180822032Y

Author

Amirgaliyev, Yedilkhan ; Berikov, Vladimir ; Cherikbayeva, Lyailya S. et al. / Group approach to solving the tasks of recognition. In: Yugoslav Journal of Operations Research. 2019 ; Vol. 29, No. 2. pp. 177-192.

BibTeX

@article{0f477baedb134462b5be8a7a51796587,
title = "Group approach to solving the tasks of recognition",
abstract = "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.",
keywords = "Classification, Hyper Spectral Image, Recognition, Semi-Supervised Learning",
author = "Yedilkhan Amirgaliyev and Vladimir Berikov and Cherikbayeva, {Lyailya S.} and Konstantin Latuta and Kalybekuuly Bekturgan",
year = "2019",
month = jan,
day = "1",
doi = "10.2298/YJOR180822032Y",
language = "English",
volume = "29",
pages = "177--192",
journal = "Yugoslav Journal of Operations Research",
issn = "0354-0243",
publisher = "University of Belgrade",
number = "2",

}

RIS

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