The use of pattern recognition methods in tasks of biomedical diagnostics and forecasting

Zhuravlev, Yu. I.; Кузнецова, А. В.; Ryazanov, Vladimir; Sen’ko, O. V.; Botvin, M.

doi:10.1134/s1054661808020028

Cited by 13 publications

(9 citation statements)

References 2 publications

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“…After that, for all tables were solved the supervised classification (recognition) problems using different algorithms. We used the implementation of algorithms in a software system " Recognition" [8]. Experiments were conducted with model data and with one practical problem.…”

Section: The Results Of Numerical Experimentsmentioning

confidence: 99%

Some Imputation Algorithms for Restoration of Missing Data

Ryazanov

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. The problem of reconstructing the feature values in samples of objects given in terms of numerical features is considered. The three approaches, not involving the use of probability models and a priori information, are considered. The first approach is based on the organization of the iterative procedure for successive elaboration of missing values of attributes. In this case, the analysis of local information for each object with missing data is fulfilled. The second approach is based on solving an optimization problem. We calculate such previously unknown feature values for which there is maximum correspondence of metric relations between objects in subspaces of known partial values and found full descriptions. The third approach is based on solving a series of recognition tasks for each missing value. Comparisons of these approaches on simulated and real problems are presented.

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Section: The Results Of Numerical Experimentsmentioning

confidence: 99%

Some Imputation Algorithms for Restoration of Missing Data

Ryazanov

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…So, according to (3), (4) Another classification of Data Mining methods is based on different approaches to teaching mathematical mo dels (Fig. 4, 5) [14][15][16][17][18].…”

Section: Methods Of Researchmentioning

confidence: 99%

Big Data analytics ontology

Lytvyn

Vysotska

Veres

et al. 2017

TAPR

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“…The following tasks may be solved by means of data mining methods and technologies , (Paklin, 2009), (Duke, 2001), (Zhuravlev, 2006), (Zinovev, 2000), (Chubukova, 2006), (Sitnik, 2007), (Witten, 2011):…”

Section: Data Mining Methodsmentioning

confidence: 99%

“…Let us describe several research methods and disciplines related to Big Data technology (Zhuravlev, 2006), (Zinovev, 2000), (Chubukova, 2006), (Sitnik, 2007), (Witten, 2011), (Marr, 2015), (Einav, 2013), (Vanyashin, 2013), (Serov, 2012), (Ronen, 2014), (Aflalo, 2013), (Gadepally, 2014), (Weinstein, 2014), (Paklin, 2013).…”

Section: Other Technologies and Research Techniquesmentioning

confidence: 99%

“…3). The following classification scheme is based on various approaches to mathematical models of learning , (Paklin, 2009), (Duke, 2001), (Zhuravlev, 2006), (Zinovev, 2000), (Chubukova, 2006), (Sitnik, 2007), (Witten, 2011). , (Paklin, 2009), (Duke, 2001), (Zhuravlev, 2006), (Zinovev, 2000), (Chubukova, 2006), (Sitnik, 2007), (Witten, 2011), (Marr, 2015), (Einav, 2013), (Vanyashin, 2013), (Serov, 2012), (Ronen, 2014), (Aflalo, 2013), (Gadepally, 2014), (Weinstein, 2014), (Paklin, 2013).…”

Section: Lytvyn V Ontology Of Big Data Analyticsmentioning

confidence: 99%

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