Group approach to solving the tasks of recognition

Yedilkhan Amirgaliyev, Vladimir Berikov, Lyailya S. Cherikbayeva, Konstantin Latuta, Kalybekuuly Bekturgan

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)177-192
Number of pages16
JournalYugoslav Journal of Operations Research
Volume29
Issue number2
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Classification
  • Hyper Spectral Image
  • Recognition
  • Semi-Supervised Learning

OECD FOS+WOS

  • 1.01 MATHEMATICS

State classification of scientific and technological information

  • 27 MATHEMATICS

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