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, and chemistry. John Wiley & Sons. Hutchinson G.E., Löffler H., 1956, The thermal classification of lakes, Proc. Nat. Acad. Sci., Washington, 42: 84-86. Järvet A, 2002, Climatological calendar of Estonian lakes and its longterm changes. - Nordic Hydrological Programme, Report No. 47, 2, 677-687. Jędrasik J., 1985, Uwarunkowania cykli termicznych w jeziorach, Zesz. Nauk. Wydz. Biologii i Nauk o Ziemi Uniw. Gdańskiego, Geografia, 14, 45-56. Kilkus K., 2000, Dimiktinų ežerų terminės struktūros, Vilniaus universiteto lejdykla, 200 pp. Kitajev S.P., 1978, Klassifikacja

References World Health Organization. http://www.who.int Ayuso MJL, Vazquez BJL, Dowrick Ch et al. Depressive disorders in Europe: prevalence figures from the ODIN study. British Journal of Psychiatry 2001; 179: 308-16. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, 2000 (DSM-IV-TR), Washington, DC: American Psychiatric Association; 2000. The ICD-10 Classification of Mental and Behavioral disorders. Clinical descriptions and diagnostic guidelines. Geneva. Switzerland: World Health Organization; 1992. King

References [1] TORLAY, L., et al. 2017. Machine learning–XGBoost analysis of language networks to classify patients with epilepsy . Brain informatics, 4.3: 159. [2] ZHANG, Licheng; ZHAN, Cheng. 2017. Machine learning in rock facies classification: an application of XGBoost. In: International Geophysical Conference, Qingdao, China, 17-20 April 2017 . Society of Exploration Geophysicists and Chinese Petroleum Society, p. 1371-1374. [3] CHEN, Tianqi, et al. 2015. Xgboost: extreme gradient boosting. R package version 0.4-2 , 1-4. [4] WANG, Ling-Lie, YANG, Chen

/04/moocs-taxonomy-of-8-types-of-mooc.html 4. Conole, G. (2014). A new classification schema for MOOCs. The International Journal for Innovation and Quality in Learning, 2 (3), 65-77. Retrieved from http://empower.eadtu.eu/images/fields-of-expertise/OERsMOOCs/INNOQUAL-Issue-3-Publication-Sep-2014-FINAL-w-cover.pdf#page=72 5. Conole, G. (2015). MOOCs as disruptive technologies: strategies for enhancing the learner experience and quality of MOOCs. Revista de Educación a Distancia, 39. Retrieved from http://www.um.es/ead/red/39/conole.pdf 6. Cormier, D. (2008, October 2

References ABUSHENKO V.L., 1998, Classification. [in:] Gritsanov A.A. (ed.), The newest philosophical dictionary, V.M. Skakun Press, Minsk (in Russian). http://bookz.ru/authors/gricanov-aa/gricanov03/page-94-gricanov03.html ARMAND D.L., 1975, Science on landscape: Foundations of the theory and logical-mathematical methods. Mysl, Moscow (in Russian). ARNOLD R.W., 2002, Soil classification principles. [in:] Micheli E., Nachtergaele F.O., Jones R.J.A., Montanarella L. (eds), Soil Classification 2001. European Soil Bureau Research Report 7, EUR 20398 EN, Office for

DOI: 10.2478/bile-2014-0001 Biometrical Letters Vol. 51 (2014), No. 1, 1-12 A variant of gravitational classification Tomasz Górecki1, Maciej Luczak2 1Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznań, Poland e-mail: tomasz.gorecki@amu.edu.pl 2Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology, Śniadeckich 2, 75-453 Koszalin, Poland e-mail: mluczak@wilsig.tu.koszalin.pl Summary In this article there is proposed a new two-parametrical variant of the

References [1] F. Aiolli and A. Sperduti. A re-weighting strategy for improving margins. Artifiical Intelligence, 137:197-216, 2002. [2] N. Aronszajn. Theory of reproducing kernels. Transactions of the American Mathematical Society, 68:337-404, 1950. [3] A. Backhaus and U. Seiffert. Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size. Neurocomputing, page in press, 2014. [4] Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1):1-127, 2009. [5] B.Hammer and A

. Comput. Vision , 42(3):145–175, May 2001. [19] Premebida C. and Faria U., Diego R. and Nunes. Dynamic bayesian network for semantic place classification in mobile robotics. Autonomous Robots , 41(5), 2017. [20] Quattoni A. and Torralba A. Recognizing indoor scenes. In IEEE International Conference on Computer Vision and Pattern Recognition , pages 413 – 420, 2009. [21] Renninger L. W. and Malik J. When is scene identification just texture recognition? Vision Research , 44(19):2301–2311, September 2004. [22] Smarandache F. and Dezert J. Information fusion based on

Introduction The classification of atmospheric states into separate circulation types is a well-known tool for describing and analysing climate conditions. The main idea behind this is to move from continuous information about an atmospheric state (e.g., the pressure field on a given day) towards discrete information. This involves ordering individual atmospheric states and assigning them to groups of types with certain similarities. This is how a circulation type catalogue is created – each type is described with a value on a nominal scale. The main advantage of

-Criterion Classification and Clustering in Data Mining. International Journal of Computing & Information Siences - Vol. 4, No. 3 (2006), pp. 143-154. A. Jain, M. N. Murty and P. J. FLynn, Data Clustering: A Review. ACM Computing Surveys, Vol. 31, No. 3, September 1999, pp. 364-423. A. Konak, D. Coit, A. Smith, Multi-objective optimization using genetic algorithms: A tutorial , Elsevier Ltd, 2005. [Online]. Available: http://www.rci.rutgers.edu/~coit/RESS_2006_MOGA.pdf [Accessed: 2006]. http://www.rci.rutgers.edu/~coit/RESS_2006_MOGA.pdf W. Lu, I. Traore, Detecting New Forms of