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Construction of tree species composition map of Estonia using multispectral satellite images, soil map and a random forest algorithm

References Adermann, V. 2010. Development of Estonian National Forest Inventory. – Tomppo, E., Gschwantner, T., Lawrence, M., McRoberts, R.E. (eds.). National Forest Inventories. Heidelberg, Springer, 171–184. Arumäe, T., Lang, M. 2016. ALS-based wood volume models of forest stands and comparison with forest inventory data. – Forestry Studies / Metsanduslikud Uurimused, 64, 5–16. Barrett, B., Raab, C., Cawkwell, F., Green, S. 2016. Upland vegetation mapping using Random Forests with optical and radar satellite data. – Remote Sensing in Ecology

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Imitation learning of car driving skills with decision trees and random forests

controller, IEEE Transactions on Systems, Man and Cybernetics 26(3): 450-463. Bratko, I., Urbancic, T. and Sammut, C. (1998). Behavioural cloning of control skill, in R.S. Michalski, I. Bratko and M. Kubat (Eds.), Machine Learning and Data Mining, John Wiley & Sons, Chichester. Breiman, L. (1996). Bagging predictors, Machine Learning 24(2): 123-240. Breiman, L. (2001). Random forests, Machine Learning 45(1): 5-32. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984). Classification and Regression

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Privately Evaluating Decision Trees and Random Forests

encrypted medical data. Journal of Biomedical Informatics, 50:234-243, 2014. [17] R. Bost, R. A. Popa, S. Tu, and S. Goldwasser. Machine learning classification over encrypted data. In NDSS, 2015. [18] Z. Brakerski, C. Gentry, and V. Vaikuntanathan. (Leveled) fully homomorphic encryption without bootstrapping. In ITCS, pages 309-325, 2012. [19] L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001. [20] J. Brickell, D. E. Porter, V. Shmatikov, and E. Witchel. Privacy-preserving remote diagnostics. In

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Low and high grade glioma segmentation in multispectral brain MRI data

References [1] S. B. Akers, Binary decision diagrams, IEEE Trans. Computers C-27 , 6 (1978) 509–516. ⇒ 115 [2] A. J. Asman, B. A. Landman, Out-of-atlas labeling: a multi-atlas approach to cancer segmentation, Proc. IEEE International Symposium on Biomedical Imaging , Barcelona, Catalunya, 2012, pp. 1236–1239. ⇒ 111 [3] L. Breiman, Random forests, Machine Learning 45, 1 (2001) 5–32. ⇒ 117 [4] J. D. Christensen, Normalization of brain magnetic resonance images using histogram even-order derivative analysis, Magn. Reson. Imaging 21

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An Influence Prediction Model for Microblog Entries on Public Health Emergencies

research works on microblog influence are abundant. However, research on the influence of microblog in specific fields, such as public health emergencies, is relatively insufficient. This study attempts to propose a microblog influence prediction model for public health emergencies, which is composed of user, time, and content features and which uses the random forest method ( Breiman, 2001 ) and the Best Match 25-based latent Dirichlet allocation model (LDA-BM25) ( Li, 2013 ). As this model is constructed specifically for public health emergencies, it highlights the

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Data mining methods for prediction of air pollution

References Agirre-Basurko, E., Ibarra-Berastegi, G. and Madriaga, I. (2006). Regression and multilayer perceptron-based models for forecast hourly O3 and nO2 levels in the Bilbao area, Environmental Modelling and Software 21(4): 430-446. Bhanu, B. and Lin, Y. (2003). Genetic algorithm based feature selection for target detection in SAR images, Image and Vision Computing 21(4): 591-608. Breiman, L. (2001). Random forests, Machine Learning 45(11): 5-32. Brunelli, U., Piazza, V., Pignato, L. and

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Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear

–131. Box, G.E.P. & Jenkins, G.M. (1976). Time series analysis: Forecasting and control , Holden-Day, San Francisco 1976. Breiman, L. (2000). Random forests. Journal Machine Learning , 45, 1, pp. 5–32. Chuchro, M. (2009). Prediction of the sewage treatement plant inflow parameters , Akademia Górniczo-Hutnicza, Wydział Geologii, Geofizyki i Ochrony Środowiska, Kraków 2009. (in Polish) Dellana, S.A. & West, D. (2009). Predictive modeling for wastewater applications: Linear and nonlinear approaches, Environmental Modelling and Software , 24, 1, pp. 96

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Impacts of forest spatial structure on variation of the multipath phenomenon of navigation satellite signals

nonlinear modeling: application to GPS positioning in urban canyons. IEEE Transactions on Signal Processing , 60 (4), 1638–1655. Ragheb, A.E., Clarke, P.J., Edwards, S.J. 2007. GPS sidereal filtering: coordinate-and carrier-phase-level strategies. Journal of Geodesy , 81 (5), 325–335. Rai, B. 2017. Feature selection and predictive modeling of housing data using random forest. International Journal of Industrial and Systems Engineering , 11 (4), 5. Robakowski, P., Wyka, T., Samardakiewicz, S., Kierzkowski, D. 2004. Growth, photosynthesis, and needle

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Morphological classification of conspecific birds from closely situated breeding areas – A case study of the Common Nightingale

-Western Journal of Zoology 9(2): 365–373. Berggren, Å. & Low, M. 2006. Sexual dichromatism in North Island Robins (Petroica longipes) is weakened by delayed plumage maturation in males and females. – Emu 106(3): 203–209. DOI: 10.1071/MU05057 Breiman, L. 2001. Random Forests. – Machine Learning 45(1): 5–32. DOI: 10.1023/A:1010933404324 Busse, P. 1967. Application of the numerical indexes of the wing-shape. – Notatki Ornitologiczne 8(1): 1–8. Busse, P. 2000. Bird Station Manual. SE European Bird Migration Network. – University of Gdańsk, pp. 264

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Impact of applied silvicultural systems on spatial pattern of hornbeam-oak forests

., Berger, F., Fuhr, M., Köhl, M., 2009: Implications of coppice stand characteristics on the rockfall protection function. Forest Ecology and Management, 259:124–131. Kint, V., 2005: Structural development in ageing temperate Scots pine stands. Forest Ecology and Management, 214:237–250. Kopecký, M., Hédl, R., Szabó, P., 2013: Non-random extinctions dominate plant community changes in abandoned coppices. Journal of Applied Ecology, 50:79–87. Králíček, I., Vacek, Z., Vacek, S., Remeš, J., Bulušek, D., Král, J. et al., 2017: Dynamics and structure of

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