Geodesic
Group Approach to Solving the Tasks of Recognition
Yugoslav journal of operations research, Tome 29 (2019) no. 2, p. 177 .

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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.
Classification : 90B85, 90C26
Keywords: Recognition, Classification, Hyper Spectral Image, Semi-Supervised Learning 
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     author = {Yedilkhan Amirgaliyev and Vladimir Berikov and Lyailya S. Cherikbayeva and Konstantin Latuta and Kalybekuuly Bekturgan},
     title = {Group {Approach} to {Solving} the {Tasks} of {Recognition}},
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Yedilkhan Amirgaliyev; Vladimir Berikov; Lyailya S. Cherikbayeva; Konstantin Latuta; Kalybekuuly Bekturgan. Group Approach to Solving the Tasks of Recognition. Yugoslav journal of operations research, Tome 29 (2019) no. 2, p. 177 . http://geodesic.mathdoc.fr/item/YJOR_2019_29_2_a1/