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Criteria of Thermal Classifications of Lakes

pp. Häkanson L., Jan sson M., 1983, Principles of Lake Sedimentology, Springer Verlag, Heidelberg, 316 pp. Hutchinson G.E., 1957, A treatise on limnology, vol. 1. Geography, physics, 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

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Coexisting Depression and Anxiety: Classification and Treatment

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

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Ontology Building Using Classification Rules and Discovered Concepts

REFERENCES [1] A. Nicola, M. Missikoff, R. Navigli, “A software engineering approach to ontology building. Information Systems,” vol. 34, issue 2, April 2009, pp. 258–275. [2] H. Gorskis, J. Čižovs, “Ontology Building Using Data Mining Techniques,” Information Technology and Management Science, vol. 15, 2012, pp.183–188. [3] I. Polaka, A. Kirshners, H. Gorskis, M. Leja, “The use and modification of decision tree classification algorithm for gastric cancer

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A universal soil classification system from the perspective of the General Theory of Classification: a review

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

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Ontology-Based Classification System Development Methodology

REFERENCES [1] T.M. Mitchell, Machine learning . McGraw-Hill, 1997, 414 p. [2] J.R. Quinlan, C4.5: Programs for Machine Learning . Morgan Kaufmann Publishers, 1993. [3] L. Rokach and O. Maimon, Data mining with decision trees: theory and applications . World Scientific Pub Co Inc., 2008. [4] L. Breiman, J.H. Friedman, R. Olshen and C.J. Stone, Classification and regression trees . Belmont, CA: Wadsworth, 1984. [5] D. Gašević, D. Djurić and V. Devedžić, Model driven architecture and ontology development. Springer-Verlag, 2006

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Place Classification using Dempster-Shafer Theory

] Oliva A. and Torralba A. Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. 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

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Feature Ranking by Classification Accuracy Estimation of Multiple Data Samples

References [1] X. Liu, A. Krishnan, and A. Mondry, “An entropy-based gene selection method for cancer classification using microarray data”, in BMC Bioinformatics, vol. 6, no. 76, 2005. [2] N. Novoselova and I. Tom, Methods for gene expression analysis. Survey and perspective directions. LAMBERT Academic Publishing GmbH&Co, 2012, 68 p. [3] E.R. Dougherty, J. Hua, and C. Sima, “Performance of feature selection methods”, in Curr. Genomics, vol.10, 2009, pp. 365-374. [4] Y. Wang, I.V. Tetko

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Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

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

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Modified, threshold-based circulation type classification for Central Europe, on the basis of Lityński’s classification

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

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Mining Online Store Client Assessment Classification Rules with Genetic Algorithms

-276. W. W. Cohen, Fast Effective Rule Induction , "Machine Learning: Proceedings of the Twelfth Conference" (ML95). California, 1995, pp. 115-123. S. Dehuri, A. Ghosh and R. Mall, Genetic Algorithms for Multi-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

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