Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://ena.lp.edu.ua:8080/handle/ntb/39414
Назва: Building computer vision systems using machine learning algorithms
Автори: Boyko, N.
Sokil, N.
Приналежність: Lviv Polytechnic National University
Бібліографічний опис: Boyko N. Building computer vision systems using machine learning algorithms / N. Boyko, N. Sokil // Econtechmod : an international quarterly journal on economics in technology, new technologies and modelling processes. – Lublin ; Rzeszow, 2017. – Volum 6, number 2. – P. 15–20. – Bibliography: 20 titles.
Журнал/збірник: Econtechmod
Том: Volum 6, number 2
Дата публікації: 2017
Видавництво: Commission of Motorization and Energetics in Agriculture
Країна (код): PL
Місце видання, проведення: Lublin ; Rzeszow
Теми: algorithm
information system
neural network
machine learning
client-server architecture
script
artificial system
machine learning algorithm
Кількість сторінок: 15-20
Короткий огляд (реферат): In this paper theoretic aspects of machine learning system in the field of computer vision is considered. There are presented methods of behavior analysis. There are offered tasks and problems associated with building systems using machine learning algorithm. The paper provides signs of problems that can be solved by using machine learning algorithms There is demonstrated step by step construction of computer vision system. The paper provides the algorithm of solving the problem of binary (two classes) classification for demonstration the machine learning algorithm possibilities in image recognition field, which can recognize the gender of the person on the photo. Aspects related to the search of data processing are also considered. There is analyzed the search of optimal parameters for algorithms. An interpretation of results in machine learning algorithm is provided. Binarization methods in machine learning algorithm are offered. There is analyzed the technology for improving the accuracy of machine learning algorithm. There are proposed ways to improve computer vision system in neural systems. Also there are analyzed large software modules that work using machine learning systems. The article provides prospects of powerful information technologies, which are necessary for the proper data selection in learning and configuration of feature extraction algorithm to create a computer vision system.
URI (Уніфікований ідентифікатор ресурсу): http://ena.lp.edu.ua:8080/handle/ntb/39414
References: 1. Boyko N. 2016 Basic concepts of dynamic recurrent neural networks development / N. Boyko, P. Pobereyko // ECONTECHMOD : an international quarterly journal on economics of technology and modelling processes, Lublin: Polish Academy of Sciences, Vol. 5, No. 2, pp. 63–68. 2. Coelho L. 2013 Building Machine Learning Systems with Python / Luis Pedro Coelho, Willi Richert, Birmingham – Mumbai: Published by Packt Publishing Ltd., 290 p. 3. Bishop C. M. 2006 Pattern recognition and machine learning / Christopher M. Bishop, Springer Science+Business Media, LLC, 78 p. 4. .Lytvyn V. 2012 Method of automation building and evaluation of data knowledge quality. / V. V. Lytvyn, M. J. Hopyak, A. B. Demchuk // Automation system. Harkiv : XNYRE, No. 161, pp. 62–69 (in Ukrainian). 5. Demchuk A. B. 2011 The method of ontological agent building on subject area A. B. Demchuk, V. V. Lytvyn, M. N. Voychyshen // Informational systems and networks. Lviv: Lviv Polytechnic Publishing House, No. 715, pp. 215–225 (in Ukrainian). 6. Palagin A. V. 2006 The architecture of ontological computer systems / A. V. Palagin// Cybernetics and system analysis. – Moscow: Cybernetics and system analysis, No. 2, pp. 111–125 (in Russian). 7. Nivikov P. S. 1973 Basis in logic, 2 edition/ P. S. Novikov, Moscow : Nauka, 400 p. (In Russian). 8. Gilbert D. 1947 The basis of theoretical logic / D.Gilbert, V. Akkeman. Moskva: GIIL, 302 p. (in Russian). 9. Elkan C. 2003 Using the triangle inequality to accelebrate k-means / C. Elkan // In Proceedings of the Twelfth International Conference on Machine Learning, pp. 147–153. 10. Demchuk A. B. 2014 Videocontent for the blind: the method tyflokomentuvannya / A. B. Demchuk // Radioelektronika, informatyka, upravlinnya, No. 1 (30), pp. 146–149 (in Ukrainian) 11. Matov O. Ia. 2009 Modern technologies of information resources integration / O. Ia. Matov // Registration, storage and processing of data, Vol. 11, No. 1, pp. 33–42. 12. Khramova I. O. 2009 The use of service-oriented architectures in the integration of information resources / I. O. Khramova // Registration, storage and processing of data, Vol. 11, No. 2, pp. 70–76. 13. Matov O. Ia. 2009 Mathematical models of conflict losses performance of the mediators ontology for General use in GRID environment / O. Ia. Matov // Registration, storage and processing of data, Vol. 11, No. 3, pp. 18–25. 14. Matov O. Ia. 2007 The problem of horizontal integration of information resources in a multi-tiered organizational structures with dynamic configuration / O. Ia. Matov // Registration, storage and processing of data, Vol. 9, No. 3, pp. 88–97. 15. Matov O. Ia. 2006 Dynamic integration of information resources of the unified information infrastructure of the electricity market / O. Ia. Matov // The functioning and development of electricity and gas markets: collection of scientific works Institute of modelling in energy im. H. Ie. Pukhova, pp. 93–98. 16. Matov O. Ia. 2006 Model performance the operating nodes of the information infrastructure of corporate information systems in the field of electricity / O. Ia. Matov // Information technology in power engineering: collection of scientific works Institute of modelling in energy im. H. Ie. Pukhova, pp. 95–105. 17. Matov O. Ia. 2006 The organization of ontologies in common use in the integrated information infrastructure preparation of data for decision-making / O. Ia. Matov // The functioning and development of electricity and gas markets: collection of scientific works Institute of modelling in energy im. H. Ie. Pukhova, pp. 99–103. 18. Matov O. Ia. 2005 The problem of the use of GRID technologies as the basis of integration of information and analytical resources to support processes of electronic control / O. Ia. Matov // Proceedings of the Academy of engineering Sciences of Ukraine, No. 2 (25), pp. 82–89. 19. Boyko N. 2016 A look trough methods of intellectual data analysis and their applying in informational systems / Nataliya Boyko // Computer sciences and information technologies CSIT 2016: Proc. of XI International scientific practical conference CSIT 2016: proceedings, Lviv: Lviv Polytechnic Publishing House, pp. 183–185. 20. Boyko N.I. 2016 Data processing technologies in dynamic systems / N. I. Boyko // Modern problems of applied mathematics and informatics., Lviv: Lviv National University named by Ivan Franko, pp. 37–40 (in Ukrainian).
Тип вмісту : Article
Розташовується у зібраннях:Econtechmod. – 2017. – Vol. 6, No. 2

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