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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. 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

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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. http://dx.doi.org/10.1016/j.is.2008.07.002 [2] H. Gorskis, J. Čižovs, “Ontology Building Using Data Mining Techniques,” Information Technology and Management Science, vol. 15, 2012, pp.183–188. http://dx.doi.org/10.2478/v10313-012-0024-5 [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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Impact of Data Normalization on Classification Model Accuracy

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

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A MOOC Taxonomy Based on Classification Schemes of MOOCs

://donaldclarkplanb.blogspot.co.uk/2013/04/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

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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). 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

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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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