Breiman, L. 1996. “Bagging Predictors.” Machine Learning no. 24 (2):123–140. doi: 10.1023/A:1018054314350.
DOI: https://doi.org/10.1007/BF00058655
Breiman, L. 2001. “Random Forests.” Machine Learning no. 45 (1):5–32. doi: 10.1023/A:1010933404324.
DOI: https://doi.org/10.1023/A:1010933404324
Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone. 1984. Classification and Regression Trees, The Wadsworth Statistics/Probability Series. Belmont, Calif.: Wadsworth International Group.
Carmone Jr, F.J., A. Kara, and S. Maxwell. 1999. “HINoV: A New Model to Improve Market Segment Definition by Identifying Noisy Variables.” Journal of Marketing Research no. 36 (4):501–509. doi: 10.2307/3152003.
DOI: https://doi.org/10.1177/002224379903600408
Enas, G.G., and S.C. Choi. 1986. “Choice of the Smoothing Parameter and Efficiency of K-Nearest Neighbor Classification.” Computers & Mathematics with Applications-Part A no. 12 (2):235–244. doi: 10.1016/0898–1221(86)90076–3.
DOI: https://doi.org/10.1016/0898-1221(86)90076-3
Fisher, R.A. 1936. “The Use of Multiple Measurements in Taxonomic Problems.” Annals of Eugenics no. 7 (2):179–188. doi: 10.1111/j.1469–1809.1936.tb02137.x.
DOI: https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Freund, Y., and R.E. Schapire. 1997. “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting.” Journal of Computer and System Sciences no. 55 (1):119–139. doi: 10.1006/jcss.1997.1504.
DOI: https://doi.org/10.1006/jcss.1997.1504
Haralick, R.M. 1979. “Statistical and Structural Approaches to Texture.” Proceedings of the IEEE no. 67 (5):786–804. doi: 10.1109/Proc.1979.11328.
DOI: https://doi.org/10.1109/PROC.1979.11328
Haralick, R.M., Shanmuga.K, and I. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems Man and Cybernetics no. Smc3 (6):610–621. doi: 10.1109/Tsmc.1973.4309314.
DOI: https://doi.org/10.1109/TSMC.1973.4309314
Hellwig, Z. 1968. “On the Optimal Choice of Predictors.” In Toward a System of Quantitative Indicators of Components of Human Resources Development, edited by Z. Gostkowski. Paryż: UNESCO.
Hothorn, T., and B. Lausen. 2005. “Bundling Classifiers by Bagging Trees.” Computational Statistics & Data Analysis no. 49 (4):1068–1078. doi: 10.1016/j.csda.2004.06.019.
DOI: https://doi.org/10.1016/j.csda.2004.06.019
Hu, Y., and T.J. Dennis. 1994. “Textured Image Segmentation by Context Enhanced Clustering.” IEE Proceedings-Vision Image and Signal Processing no. 141 (6):413–421. doi: 10.1049/ip-vis:19941548.
DOI: https://doi.org/10.1049/ip-vis:19941548
Koronacki, J., and J. Ćwik. 2005. Statystyczne systemy uczące się. Warszawa: Wydawnictwa Naukowo-Techniczne.
Lerski, R.A., K. Straughan, L.R. Schad, D. Boyce, S. Bluml, and I. Zuna. 1993. “MR Image Texture Analysis — an Approach to Tissue Characterization.” Magnetic Resonance Imaging no. 11 (6):873–887. doi: 10.1016/0730–725x(93)90205-R.
DOI: https://doi.org/10.1016/0730-725X(93)90205-R
Liao, S.H., P.H. Chu, and P.Y. Hsiao. 2012. “Data Mining Techniques and Applications — A Decade Review from 2000 to 2011.” Expert Systems with Applications no. 39 (12):11303–11311. doi: 10.1016/j.eswa.2012.02.063.
DOI: https://doi.org/10.1016/j.eswa.2012.02.063
Ligęza, A. 2006. Logical Foundations for Rule-Based Systems, Studies in Computational Intelligence. Berlin – New York: Springer.
DOI: https://doi.org/10.1007/3-540-32446-1
Omiotek, Z., A. Burda, and W. Wójcik. 2013. “The Use of Decision Tree Induction and Artificial Neural Networks for Automatic Diagnosis of Hashimoto’s Disease.” Expert Systems with Applications no. 40 (16):6684–6689. doi: 10.1016/j.eswa.2013.03.022.
DOI: https://doi.org/10.1016/j.eswa.2013.03.022
Omiotek, Z., A. Burda, and W. Wójcik. 2015. “Application of Selected Classification Methods for Detection of Hashimoto’s Thyroiditis on the Basis of Ultrasound Images.” In Computational Intelligence, Medicine and Biology: Selected Links, edited by K. Pancerz and E. Zaitseva, 23–37. Cham u.a.: Springer.
DOI: https://doi.org/10.1007/978-3-319-16844-9_2
Omiotek, Z., and W. Wójcik. 2014. “Zastosowanie metody Hellwiga do redukcji wymiaru przestrzeni cech obrazów USG tarczycy.” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (3):14–17.
DOI: https://doi.org/10.5604/20830157.1121333
Tadeusiewicz, R. 1993. Sieci neuronowe. 2nd ed., Problemy Współczesnej Nauki i Techniki Informatyka. Warszawa: Akademicka Oficyna Wydawnicza RM.
Walesiak, M. 2005. “Problemy selekcji i ważenia zmiennych w zagadnieniu klasyfikacji.” Prace Naukowe AE we Wrocławiu. Taksonomia 12 (1076):106–118.
Walesiak, M., and E. Gatnar. eds. 2009. Statystyczna analiza danych z wykorzystaniem programu R. Warszawa: Wydawnictwo Naukowe PWN.