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Issue 4 (190), article 5

DOI:https://doi.org/10.15407/kvt190.04.073

Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Kalnysh V.V.1, Dr (Biologi), Professor,
Head of laboratory of labor psychophysiology
e-mail: vkalnysh@ukr.net
Stasyshyn R.O.1, graduate student
e-mail: rokstasyshyn@gmail.com
Oliskevych M.O.2, Dr (Economics), Associate Professor,
Professor at the Dept of Mathematical Economics and Econometrics,
e-mail: olisk@ukr.net
1SI «Institute for occupational health of the National Academy of Medical Sciences of Ukraine»
75, Saksagansky str., Kyiv, Ukraine, 01033
2Faculty of Mechanics and MathematicsIvan Franko National University of Lviv,
1, Universytetska Str., Lviv, 79000, Ukraine

QUALITY CHARACTERISTICS FOR EMOTIONAL REGULATION OF EMPLOYEES PROFESSIONAL ACTIVITY AT WORK WITH HIGH DANGER IN ELECTRIC POWER INDUSTRY

Introduction. There are a number of occupations in Ukraine, including electric power industry occupations, where people work in high-risk conditions. These conditions impose on the workers a significant amount of restrictions on their functions and professionally important qualities, which leads to the emergence of certain requirements in the process of professional selection and monitoring. One of the main requirements to the staff in potentially dangerous objects is the high level of readiness to work in difficult situations. This implies the presence of relevant professional qualities among the employees of these occupations, one of the most important is emotional stability, which closely links with the maintenance of work ability and attention in the conditions of responsible work accomplishing.
The purpose of the article is to develop an approach for quantifying the deviation from the “proper” level of emotional regulation and to identify on this basis the structure of the relevant reactions distribution for locksmiths of operational-outgoing brigades at electric power industry.
Methods. The survey of workers was conducted by means of SOPAS-8 method, which was adapted for a comprehensive study of the individual mental stability to the extreme activity factors impact. According to this method, eight factors of the mental state was identified: mental rest, feeling of satisfaction; strength and energy sensitivity; desire for action; impulsive reactivity; mental depression and exhaustion feeling; depressed mood; mental anxiety or indignation, stress; anxiety and fear feeling. Materials of psycho-physiological observations was analyzed using methods of variation statistics, multivariate analysis, regression binary models.
Results. A modeling concept based on the multi-factor regression probit model was developed. Developed approach allows to estimate the risk of a deterioration in the quality of emotional regulation for each individual employee based on information about the factors observed values for his emotional state regardless of age.
Conclusions. The statistically significant influence on the risk of an employee’s emotional state deterioration create the characteristics of strength and energy sensitivity, impulsive reactivity, mental depression and exhaustion feeling levels. Our investigation revealed that belonging to different age groups is not a significant factor to affect the risk of deterioration in the emotional regulation quality for electricians at operational-outgoing brigades.
Keywords: regression probit model, emotional state, electricians, high danger.

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REFERENCES

1 Kalnysh V.V. Ways of professional psychophysiological selection improving and professionally important qualities monitoring for specialists, which work in increased danger conditions. Ukrainian Journal of Occupational Health Problems. 2015. No 4(45). P. 14–25. (in Russian).

2 Fomin N.V., Kosyakov R.V. Problems of psychological deprivation of energy engineers working on a rotational basis. Living psychology. 2016. Vol. 3. Issue 1. P. 75–82. (in Russian).

3 Kostina Yu.S., Mironova E.P. Functional states and worker adaptation. Scientific Journal “Universum: Psychology and Education”. 2016. N 6(24). URL: http//7universum.com/ ru/psy/archive/item/3275. (in Russian).

4 Iluhina V.A. Psychophysiology of functional states and cognitive activity of a healthy and sick person. Moscow: N-L. 2012. 368 p. (in Russian).

5 Gorunova L.N., Kruglova M.A., Gorodetskaya E.N., Butina T.N., Veretshagina L.A., Pogrebitshkaya V.E. Professional stress: development of staff professional stability for potentially dangerous objects. Petersburg Psychological Journal. 2017. No 18. P. 89–111. (in Russian).

6 Hall G.B., Dollard M.F., Winefield A.H., Dormann C., Bakker A.B. Psychosocial safety climate buffers effects of job demands on depression and positive organizational behaviors. Anxiety Stress Coping. 2013. V. 26(4). P. 355–377.
https://doi.org/10.1080/10615806.2012.700477

7 Fida R., Paciello M., Tramontano C., Barbaranelli C., Farnese M.L. “Yes, I Can”: the protective role of personal self-efficacy in hindering counterproductive work behavior under stressful conditions. Anxiety Stress Coping. 2015. V. 28(5). P. 479–499.
https://doi.org/10.1080/10615806.2014.969718

8 Gaponova G.I. Psychological training of a fire safety engineer: about personal factors of behavioral regulation in risk situations. Emergencies: industrial and environmental safety. 2014. No 3–4(19-20). P. 10–20. (in Russian).

9 Kalnysh V.V., Pashkovsky S.M., Stasyshyn R.O. The ways of improving the psychophysiological selection and monitoring of professionally important qualities of operators. Ukrainian journal of medicine, biology and sport. 2017. No 2(4). P. 149–160. (in Russian).

10 Kusnezova M.M. Features of emotionally-volitional regulation of educational activity in students with an optimistic attributive style. Visnik Harkivskogo nacionalnogo pedagogicnogo universitetu imeni G.S. Skovorodi. Psihologia. 2017. Vol. 55. P. 82–102. (in Russian).

11 Ekman P. Psychology of emotions. I know what you feel. St. Petersburg: Peter. 2010. 334p. (in Russian).

12 Kosub Ya.V., Kusnezov M.A. Emotional attitude to the teaching of students with different types of motivational regulation activity. Scientific Journal of Kherson State University. Series: Psychological Sciences. 2015. Vol. 6. P. 86–93. (in Russian)

13 Foruard S. Emotional blackmail. Moscow: AST, AST Moscow. 2005. 320 p. (in Russian).

14 Dolgova V.I., Melnyk E.V. Empathy. Moscow: Publishing house “Pen”. 2014. 185 p. (in Russian).

15 Kemeron-Bendler L., Lebo M. Hostage emotions. How to save your emotional life. Voronezh: Publishing house of the NGO “MODEK”. 1999. 256 p. (in Russian).

16 Sopov V.F. Mental conditions in strenuous professional activity. M.: Academic Project; Triksta. 2005. 128 p. (in Russian).

Received 04.09.2017

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Issue 4 (190), article 4

DOI:https://doi.org/10.15407/kvt190.04.056

Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Antomonov M.Y.1, D. Biol. Sci., Professor,
Chief Researcher, Laboratory of Epidemiological Research and Medical Informatics
e-mail: antomonov@ukr.net
Shevchenko A.A.2, Professor,
Head of Research Center of State University “Dnipropetrovsk Medical Academy of Ministry of Health of Ukraine”
e-mail: toxysan@ukr.net
Kulagin A.A.3, Ph.D (Med. Sci.),
Professor of the Department of Hygiene and Ecology
e-mail: kulagin111188@ukr.net
1O.M. Marzeev Institute for Public Health, NAMS of Ukraine,
Ukraine, 02660 Kyiv-94, Popudrenko Str.
2Research Center of State University “Dnipropetrovsk Medical Academy of Ministry of Health of Ukraine”,
Ukraine, 49027, Dnipro, Vernadsky Str., 9
3Dnipropetrovsk Medical Academy of the Ministry of Health of Ukraine
Ukraine, 49027, Dnipro, Soborna Square, 4

ALGORITHMS OF MULTIFACTORIAL REGRESSION MODELING IN ECOLOGICAL AND HYGIENIC STUDIES

Introduction. One of the most urgent problems of environmental health is soil contamination by oil and petroleum products (PP) and its impact on crop productivity and population health. The main task here is to determine the acceptable or safe concentrations of PP in the soil. However, at present time there is no unified approach of PP regulating in different countries. One possible solution of the problem is mathematical modeling of the results of experimental studies. With the help of mathematical models, it becomes possible not only to describe the investigated dependencies, but also to calculate safe levels of PP in the soil.
The purpose of the paper is to develop a methodology for constructing and using multifactor nonlinear regression models from data obtained in a real ecological and biological experiment.
Results. The article presents the results of an experimental study of the influence of one type of petroleum products — diesel fuel, when it enters the soil to germinate one of the most important crops — wheat. It is shown that the mathematical model describing the suppression of wheat growth should be a multifactorial function (“time — concentration — effect”), which has a nonlinear character. An algorithm for constructing multifactorial nonlinear regression models is proposed. On the basis of experimental data, an adequate multifactor nonlinear mathematical model was developed. This made it possible to calculate the threshold concentration of diesel fuel, which does not affect the growth of wheat.
Conclusion. On the basis of the proposed algorithm from experimental data an adequate multifactor nonlinear regression model was constructed. With the help of this model, the concentration of diesel fuel that does not cause a negative effect on the growth of wheat was calculated.
Keywords: petrolproducts, environmental contamination, threshold concentration, mathematical modeling, nonlinear multifactor regression models.

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Reseived 01.08.2017

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Issue 4 (190), article 3

DOI:https://doi.org/10.15407/kvt190.04.033

Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Melnichuk S.V., Dr (Engineering),
Researcher of Dynamic Systems Control Ddepartment
e-mail: sergvik@ukr.net
Gubarev V.F., Professor,
Dr (Engineering), Professor,
Corresponding Member of NAS of Ukraine,
Head of Dynamic Systems Control Department
e-mail: v.f.gubarev@gmail.com
Salnikov N.N., (Engineering),
Senior Researcher of Dynamic Systems Control Department
e-mail: salnikov.nikolai@gmail.com
Space Research Institute National Academy of Sciences of Ukraine
and State Space Agency of Ukraine,
Acad. Glushkov av. 40, 4/1, 03680, Kyiv 187, Ukraine

USING INFORMATION FEATURES IN COMPUTER VISION FOR 3D POSE ESTIMATION IN SPACE

Introduction. Autonomous rendezvous and docking is an important technological capability that enables various spacecraft missions. It requires the real-time relative pose estimation i.e. determination of the position and attitude of a target object relative to a chaser. The usage of techniques based on optical measurement has certain advantages at close range phases of docking.
The purpose of the paper is to create a computer vision system, that estimates position and attitude of the target relative to the chaser. To develop the design of a computer vision system and suited mathematical methods. To use a new learning-based method, which can be implemented for the real-time execution with limited computing power.
Methods. A non-standard approach to solving the problem was used. A combination of image processing techniques, machine learning, decision trees and piecewise linear
approximation of functions were used. The tool of informative features computed by images was essentially used.
Results. A two-stage algorithm, which involves training the computer vision system to recognize the attitude and position of the target in a changing lighting environment was developed. The calculation of the camera parameters was carried out to ensure a given accuracy of the solution of the problem.
Conclusion. It was shown that the informative features can be used to create a high-performance on-board system for estimating relative attitude and position. Implementation of the proposed algorithm allows to create a competitive device for docking in space.
Keywords: autonomous rendezvous, uncooperative pose estimation, model-based pose estimation, vision-based pose estimation, computer vision, decision tree, linear approximation, informative features, image processing, machine learning, identification, relative position and attitude estimation.

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REFERENCES

1 Gubarev V.F., et al. Using Vision Systems for Determining the Parameters of Relative Motion of Spacecrafts. Journal of Automation and Information Sciences, 2016. No11. P. 23–39.
https://doi.org/10.1615/JAutomatInfScien.v48.i11.30

2 Shi J.-F., et al. Uncooperative Spacecraft Pose Estimation Using an Infrared Camera During Proximity Operations. AIAA Space 2015 Conference and Exposition. Issue AIAA 2015–4429. 17 pp.

3 Kelsey J.M., et. al. Vision-Based Relative Pose Estimation for Autonomous Rendezvous and Docking. 2006 IEEE Aerospace Conference. 20 pp.
https://doi.org/10.1109/AERO.2006.1655916

4 Zorich V.A. Mathematical Analysis. Part 2. Moskow: Nauka, 1984. 640 p.

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6 Philip N.K., Ananthasayanam M.R. Relative position and attitude estimation and control schemes for the final phase of an autonomous docking mission of spacecraft. Acta Astronautica. 2003. Vol. 52. I. 7. P. 511–522.

7 Shijie et.al. Monocular Vision-based Two-stage Iterative Algorithm for Relative Position and Attitude Estimation of Docking Spacecraft. Chinese Journal of Aeronautics, 2010. Vol. 23. I. 2. P. 204–210.

8 Vassilieva N.S. Content-based image retrieval methods. Programming and Computer Software. 2009. Vol. 35. No 3. P. 158–180.
https://doi.org/10.1134/S0361768809030049

9 Prewitt J.M.S. Object enhancement and extraction. Picture Processing and Psychopictorics, B. Lipkin and A. Rosenfeld. New York: Academic Press. 1970. P. 75–149.

10 Sobel I., Feldman G. A 3×3 isotropic gradient operator for image processing, presented at a talk at the Stanford Artificial. Project in Pattern Classification and Scene Analysis, R. Duda and P. Hart. Eds.: John Wiley & Sons, 1968. P. 271–272.

11 Canny, J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986. No 8(6). P. 679–698.

12 Arbelaez P., et al. Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. P. 898–916.

13 Bentley J.L. Multidimensional binary search trees used for associative searching. Communications of the ACM. 1975. Vol. 18. I. 9. P. 509–517.

14 Samet H. The Design and Analysis of Spatial Data Structures. 1990. 493 p.

Received 14.06.2017

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Issue 4 (190), article 2

DOI:https://doi.org/10.15407/kvt190.04.019

Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Synytsya K.M., PhD (Engineering)
Deputy Director on Research
e-mail: ksynytsya@irtc.org.ua
International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine
Acad. Glushkova av., 40, Kiev, 03187, Ukraine

E-LEARNING  MODELS ANALYSIS FOR LIFE-LONG LEARNING

Introduction. E-learning models reflect various aspects of ICT application in education but they are not intended for reflecting long periods of time, multiple sources of information or dynamic control from the learner’s side. These and other features are important for lifelong learning modeling aimed at the use of individual’s data for raising efficiency of learning.
The purpose of the paper is to review e-learning models that describe a framework, abstract architecture or a reference model to identify potential prototype for lifelong e-learning model and to outline the requirements to its construction.
Results. The study reveals typical features of the e-learning models grouped according to the level of abstraction and connection to technology and pedagogy. It describes lifelong learning specifics and models that could be considered during the lifelong e-learning modeling, although none of the existing models could serve as a unique prototype. A set of requirements to lifelong e-learning model is suggested.
Conclusion. Lifelong e-learning model should be presented as a set of views that are relevant to actors in e-learning and reflect longevity, multiple sources, context of learning, management and learner’s control, collection and sharing the data about learning. The main purpose of it could be in identification of components, tools and processes that should be implemented for intelligent and efficient lifelong learning support
Keywords: e-learning, lifelong learning, e-learning framework, reference model, learner-centric model, requirements to e-learning model

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REFERENCES

1 ISO/IEC 2382-36. Information technology. Vocabulary. Information technology for learning, education and training.

2 Odabasi, F., Kuzu, A. & Gunuc, S. (2012). Characteristics of lifelong learner. In P. Resta (Ed.), Proceedings of SITE 2012–Society for Information Technology & Teacher Education International Conference (pp. 4037-4039). Austin, Texas, USA: Association for the Advancement of Computing in Education (AACE).

3 Lawson, M., Askell-Williams H., Murray-Harvey R. “The attributes of the lifelong learner.” A report prepared for the Queensland Studies Authority. Flinders Univ. Adelaide (2006). 101 C

4 Vaishali Suryawanshi, Dayanand Suryawanshi, Fundamentals of E-Learning Models: A Review. In Innovation in engineering science and technology (NCIEST-2015) IOSR Journal of Computer Engineering (IOSR-JCE) PP 107-120

5 Wentling T., Waight C., Gallaher J. et. al. e-learning – A Review of Literature. NCSA, Univ.Illinois, 2000, 73 p.

6 Leal, J. P., & Queiros, R. (2010). eLearning Frameworks: a survey. In Proceedings of International Technology, Education and Development Conference

7 Wilson, S., Olivier, B., Jeyes, S., Powell, A., & Franklin, T. (2004). A technical framework to support e-learning. JISC.

8 Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice. Taylor & Francis. 2nd edition

9 Glancy, F. H., & Isenberg, S. K. (2011). A Conceptual ELearning Framework. European, Mediterranean & Middle Eastern Conference on Information Systems 2011/ 636-650.

10 Mayes, T. and de Freitas, S. (2004) Review of e-learning theories, frameworks and models. London: Joint Information Systems Committee.

11 Dabbagh, N. (2005). Pedagogical models for E-Learning: A theory-based design framework. International Journal of Technology in Teaching and Learning, 1(1), 25-44.

12 Khan, B. H. (2000). A Framework for E-Learning. Distance Education Report, 4(24), 3-8.

13 Synytsya K. Adding mobility to the ADL language course (2013)The International Scientific Conference eLearning and Software for Education (ElSE). Vol 2 Pages 147-152

14 Aparicio, M., Bacao, F., & Oliveira, T. (2016). An e-Learning Theoretical Framework. Educational Technology & Society, 19 (1), 292–307.

15 Ismail, J. (2001). The design of an e-learning system: Beyond the hype. The Internet and Higher Education, 4(3), 329-336.
https://doi.org/10.1016/S1096-7516(01)00069-0

16 Synytsya, K. (2006). Standards for Learning Technologies: Overview and Directions. Communications of IICM, 8(2), 5-15.

17 Wisher R., Khan B. (2010), Learning on Demand. ADL and the Future of e-Learning.

18 Dagger, D., O’Connor, A., Lawless, S., Walsh, E., & Wade, V. P. (2007). Service-oriented e-learning platforms: From monolithic systems to flexible services. Internet Computing, IEEE, 11(3), 28-35.
https://doi.org/10.1109/MIC.2007.70

19 Seufert, S., Lechner, U., & Stanoevska, K. (2002). A reference model for online learning communities. International journal on E-learning, 1(1), 43-54.

20 McCombs, B., & Vakili, D. (2005). A learner-centered framework for e-learning. The Teachers College Record, 107(8), 1582-1600.
https://doi.org/10.1111/j.1467-9620.2005.00534.x

21 Livingstone, D. W. (2001). Adults’ informal learning: Definitions, findings, gaps and future research. WALL Working Paper No.21. 50 p.

22 Attwell, G. (2007). Personal Learning Environments-the future of eLearning?. Elearning papers, 2(1), 1-8.

23 Milligan, C.D., Beauvoir, P., Johnson, M.W., Sharples, P., Wilson, S. and Liber, O., 2006, October. Developing a reference model to describe the personal learning environment. In EC-TEL (Vol. 4227, pp. 506-511).
https://doi.org/10.1007/11876663_44.

Reseived 17.09.2017

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Issue 4 (190), article 1

DOI:https://doi.org/10.15407/kvt190.04.005

Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Grytsenko V.I., Corresponding Member of NASU of Ukraine,
Director of International research and training
center for Information technologies and systems
of the NASU and MESU
e-mail: vig@irtc.org.ua
Onyshchenko I.M., PhD (Economics),
Senior Researcher of the Department of Economic and Social
Systems and Information Technologies
e-mail: standardscoring@gmail.com
International research and training center for Information
technologies and systems of the NASU and MESU,
40, Ave Glushkov, 03680, Kiev, Ukraine

DETERMINING THE INFORMATIVITY OF PARAMETERS IN A PROGNOSTIC MODEL FOR EVALUATING THE PROBABILITY OF PRODUCT SELECTION IN THE CONDITIONS OF “BIG DATA”

Introduction. Fast growth of collected and stored data due to IT bumming caused a problem called “Big Data Problem”. Most of the new data are unstructured and this is the core reason why traditional relational data warehouse are so inefficient to deal with “Big Data”. Predicting and modeling based on “Big Data” also can be problematic because of high volume and velocity. To avoid some problems online learning algorithms can be successful for high-load systems.
The purpose of the article is to develop an approach to feature selection and modeling in case of “Big Data” with using online learning algorithm.
Method. Online learning algorithm for FTRL (Follow-The-Regularized-Leader) model with L1 and L2 regularization to select only important features was used.
Results. The approaches of modeling in cases of using batch and online learning algorithms are described on the example of online auction system. The online learning algorithm has very strong preferences in case of high load and high velocity. Mathematical background for modification of linear discriminator of FTL (Follow-The-Leader) model with adding regularization was described. L1 and L2 regularization allows us to select important features in real time. If the feature becomes useless, the regularization will set the corresponding coefficient equal to 0. But it does not remove the feature from training process and the coefficient can be restored with some value in case of its importance for model. The full process is prepared as a program in Python and can be used in practice.
The results may be applied for modeling and predicting in projects with high volume or velocity of data for example — social networks, online auctions, online gaming, recommendation systems and others.
The results may be applied for modeling and forcasting in projects with high volume or velocity of data, for example — social networks, online auctions, online gaming, recommendation systems and others .
Conclusions. FTRL model to work as online learning algorithm that allows to predict binary outcomes in high load “Big Data” systems was modified.
Getting into account that number of predictors can be enormous it takes much computing resources, time and make the process difficult. This feature selection problem was solved with using L1 regularization. The selection procedure was added to modified online learning FTRL model. L1 regularization to score the importance of predictors in real time was used.
A program that runs described mathematical algorithm was developed. Note that the algorithm effectively works with sparse matrices by analyzing incoming data and updating weights only for predictors that are presented. The algorithm has L1 and L2 regularization features that may be used for feature selection and avoid overfitting.
Keywords: information technologies in economics, economical and mathematical modeling, online learning algorithms, regularization, Big Data.

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https://doi.org/10.1145/1242572.1242643

4 Shalev-Shwartz, Shai. “Online Learning and Online Convex Optimization”. Foundations and Trends in Machine Learning. 2011. pp. 107–194.
https://doi.org/10.1561/2200000018

5 Gilles Gasso. Batch and online learning algorithms for nonconvex Neyman-Pearson classification / Gilles Gasso, Aristidis Pappaioannou, Marina Spivak, Leon Bottou / ACM Transaction on Intelligent System and Technologies, 2(3), 2011.
https://doi.org/10.1145/1961189.1961200

6 H Brendan McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization. International Conference on Artificial Intelligence and Statistics, pages 525–533, 2011.

7 Byll Franks. Ukroshchenye bolshykh dannykh: kak yzvlekat znanyia yz massyvov ynformatsyy s pomoshchiu hlubokoi analytyky / Byll Franks; per. s anhl. Andreia Baranova. — M. : Mann, Yvanov y Ferber, 2014. — 352 p. (in Russian).

8 N.B. Shakhovska. Model Velykykh Danykh “Sutnist — kharakterystyka”. / N.B. Shakhovska, Yu.Ia. Boliubash / 2015 r. [Elektronnyi resurs] — Rezhym dostupu: http://www.academia.edu/19609620/%D0%9C%D0%9E%D0%94%D0%95%D0%9B%D0%AC_%D0%92%D0%95%D0%9B%D0%98%D0%9A%D0%98%D0%A5_%D0%94%D0%90%D0%9D%D0%98%D0%A5_%D0%A1%D0%A3%D0%A2%D0%9D%D0%86%D0%A1%D0%A2%D0%AC-%D0%A5%D0%90%D0%A0%D0%90%D0%A% D0%A2%D0%95%D0%A0%D0%98%D0%A1%D0%A2%D0%98%D0%9A%D0%90_ (in Ukrainian).

9 Cherniak Leonyd. Bolshye Dannye — novaia teoryia y praktyka. Otkrytye systemy. SUBD. — M.: Otkrytye systemy, 2011. — No 10. [Elektronnyi resurs] — Rezhym dostupu: http://www.osp.ru/os/2011/10/13010990/ (in Russian).

10 Uskenbaeva, R.K. Tasks of resources provision of distributed computer systems functionality / R.K. Uskenbayeva, A.A. Kuandykov, A.U. Kalizhanova. — Dubai, World Academy of Science, Engineering and Technology. — 2012. — Iss. 70. — P. 580–581.

11 R. Bekkerman, M. Bilenko, and J. Langford. Scaling up machine learning: Parallel and distributed approaches. 2011

12 H.B. McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and L1 regularization. In AISTATS, 2011.

13 H.B. McMahan and M. Streeter. Adaptive bound optimization for online convex optimization. In COLT, 2010.

14 Hrytsenko V.I. Zastosuvannia instrumentiv Big Data dlia pidvyshchennia efektyvnosti onlain reklamy. Ekonomiko-matematychne modeliuvannia sotsialno-ekonomichnykh system. Vypusk 21. — Kyiv, 2016. P 5–21 (in Ukrainian).

15 Big Data — Wikipedia. [Elektronnyi resurs] — Rezhym dostupu: https://en.wikipedia.org/wiki/Big_data

16 Chto takoe Real-Time Bidding. [Elektronnyi resurs] — rezhym dostupu: http://konverta.ru/how (in Russian).

17 Introduction to online machine learning: Simplified. [Elektronnyi resurs] — rezhym dostupu: http://www.analyticsvidhya.com/blog/2015/01/introduction-online-machine-learning-simplified-2/

18 Riedman J. H. Regularization paths for generalized linear models via coordinate descent / Riedman J. H., Hastie T., Tibshirani R. / Journal of Statistical Software. 2010. Vol. 33, no. 1. pp. 1–22

19 L1- y L2-rehuliaryzatsyia v mashynnom obuchenyy. [Elektronnyi resurs] — rezhym dostupu: https://msdn.microsoft.com/uk-ua/magazine/dn904675.aspx (in Russian).

20 L1-rehuliaryzatsyia lyneinoi rehressyy. Rehressyia naymenshykh uhlov (alhorytm LARS). [Elektronnyi resurs] — rezhym dostupu: chrome-extension: //ecnphlgnajanjnkcmbpancdjoidceilk/content/web/viewer.html?source=extension_pdfhandler &file=http%3A%2F%2Fwww.machinelearning.ru%2Fwiki%2Fimages%2F7%2F7e%2F VetrovSem11_LARS.pdf (in Russian).

Received 28.09.2017