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., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12(85), 2825–2830. DOI: 10.3389/fninf.2014.00014 Rátky, I., Farkas, P. 2003. A növényzet hatása a hullámtér vízszállító képességére. Vízügyi Közl. 85(2), 246–264. Schaffer, C. 1993. Overfitting Avoidance as Bias. Machine Learning 10, 153–178. DOI: 10.1007/bf00993504 Steiger, J, Gurnell, A.M., Ergenzinger, P., Snelder, D.D. 2001. Sedimentation in the riparian zone of an incising river. Earth Surf. Process. Landforms 26, 91

://nlp.ffzg.hr/web-corpora-of-bosniancroatian-and-serbian-top-level-domain-published/ (7 September 2014). 15. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V. Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011), “Scikit-learn: Machine Learning in Python”, In Journal of Machine Learning Research, Vol. 12, pp. 2825-2830. 16. Raschka, S. (2015). Python machine learning, Birmingham, Packt Publishing Ltd. 17. Rubin, T. N., Chambers, A., Smyth, P., Steyvers, M. (2012), “Statistical topic models for multilabel document classification

Machine (SVM)”, School of EECS, Washington State University, 2012. [20] “1.4. Support Vector Machines,” scikit-learn, [Online]. Available: https://scikit-learn.org/stable/modules/svm.html . [Accessed 3 May 2019]. [21] A. Zhang, P. Zhang and Y. Feng, “Short-term load forecasting for microgrids based on DA-SVM,” COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 38, no. 1, pp. 68–80, 2019. https://doi.org/10.1108/COMPEL-05-2018-0221 [22] “scikit-learn,” scikit-learn developers, [Online]. Available: http://scikit-learn

REFERENCES Brian L. DeCost, Elizabeth A. Holm, 2015. A computer vision approach for automated analysisand classification of microstructural image data , Computational Materials Science, 110, 126-133, DOI: 10.1016/j.commatsci.2015.08.011 Chowdhury A., Kautz E., Yener B., Lewis D., 2016. Image driven machine learning methods formicrostructure recognition , Computational Materials Science, 123, 176-187, DOI:10.1016/j.commatsci.2016.05.034. Geron A., 2018. Uczenie maszynowe z użyciem Scikit-Learn i TensorFlow , Helion SA,Gliwice Poland [in Polish] Prasanna P

Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems . Helion. Golenia M., Zagajewski B., Ochtyra A., 2005, Zastosowanie sztucznych sieci neuronowych do aktualizacji map pokrycia terenu Corine. “Polski Przegląd Kartograficzny” T. 47, nr 3-4, pp. 257–266. Goodfellow I., Bengio Y., Courville A., 2009, Deep Learning. Boston : Massachusetts Institute of Technology (MIT). Khaze S. R., Mohammed M., Hojjatkhah S., 2013, Application of artificial neural networks in estimating participation in elections . “International Journal of

. (2011). A Bradley-Terry type model for forecasting tennis match results. International Journal of Forecasting , 27 (2), 619-630. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research , 12 (Oct), 2825-2830. Sipko, M., & Knottenbelt, W. (2015). Machine learning for the prediction of professional tennis matches. MEng computing-final year project, Imperial College London . Wei, X., Lucey, P., Morgan, S., Reid, M., & Sridharan, S. (2016

References Berrar D., 2019, Performance Measures for Binary Classification , Encyclopedia of Bioinformatics and Computational Biology, vol. 1, 546-560. Boughorbel S., Jarray F., El-Anbari M., 2017, Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric , PloS one 12.6. Ferri C., Hernández-Orallo J., Modroiu R., 2009, An experimental comparison of performance measures for classification , Pattern Recognition Letters, 30.1, 27-38. Géron A., 2017, Hands-On Machine Learning with Scikit-Learn & TensorFlow , O’Relly Sebastopol

, Stowarzyszenie Pomoc i Rozwój, Szczecin. Hwang C.L., Yoon K., 1981, Multiple Attribute Decision Making: Methods and Applications , Springer--Verlag, New York. James G., Witten D., Hastie T., Tibshirani R., 2015, An Introduction to Statistical Learning , Springer, New York. Pedregosa F. et al., 2011, Scikit-learn: machine learning in Python , Journal of Machine Learning Research, no. 12, pp. 2825-2830. Raschka S., 2018, Python. Uczenie maszynowe , Wydawnictwo Helion, Gliwice. Sawiłow E., 2009, Zastosowanie metod wielowymiarowej analizy porównawczej dla potrzeb ustalania

Science and Statistics)”, Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. [7] C. E. Rasmussen and C. K. I. Williams, “Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)”, The MIT Press, 2005. [8] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay, “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research , vol. 12 pp. 2825-2830, 2011. [9] M. H. S

think of the children?" Examining COPPA compliance at scale,” in PETS ’18 , vol. 3, 2018, pp. 63–83. [46] N. Sadeh, A. Acquisti, T. D. Breaux, L. F. Cranor, A. M. McDonald, J. R. Reidenberg, N. A. Smith, F. Liu, N. C. Russell, F. Schaub, and S. Wilson, “The usable privacy policy project,” Carnegie Mellon University, Tech. report CMU-ISR-13-119, 2013. [47] K. M. Sathyendra, S. Wilson, F. Schaub, S. Zimmeck, and N. Sadeh, “Identifying the provision of choices in privacy policy text,” in EMNLP ’17 , 2017. [48] scikit-learn developers, “sklearn