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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. 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 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 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). 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: 2Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology, Śniadeckich 2, 75-453 Koszalin, Poland e-mail: 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: [Accessed: 2006]. W. Lu, I. Traore, Detecting New Forms of