Bishop C.; Pattern recognition and machine learning, Springer, Berlin, Heidelberg, New York, 2006. Hand D., Mannila H., Smyth P.; Principles of Data Mining, MIT Press, 2001. Brodowski S., Podolak I. T.; Hierarchical Estimator, Expert Systems with Applications, 38(10), 2011, pp. 12237–12248. Hastie T., Tibshirani R., Friedman J.; The Elements of Statistical Learning, Springer, Berlin, Heidelberg, New York, 2001. Russell S. J., Norvig P.; Artificial Intelligence: A Modern Approach, Pearson Education, 2003. Christiani N., Shawe-Taylor J.; Support Vector Machines and other kernel based learning methods, Cambridge University Press, 2000. Scholkopf B., Smola A.; Learning with kernels, MIT Press, Cambridge, 2002. Schapire R.; The Strength of Weak Learnability, Machine Learning, 5(2), 1990, pp. 197– 227. Freund Y., Schapire R.; A decision theoretic generalization of online learning and an application to boosting, Journal of Computer and System Sciences, 55, 1997, pp. 119–139. Jordan M., Jacobs R.; Hierarchical mixtures of experts and the EM algorithm, Neural Computation, 1994, pp. 181–214. Saito K., Nakano R.; A constructive learning algorithm for an HME, IEEE International Conference on Neural Networks, 3, 1996, pp. 1268–1273. Quinlan J.; Learning with continuous classes, Proceedings of the 5-th Australian Conference on Artficial Intelligence, 1992, pp. 343–348. Podolak I.; Hierarchical classifier with overlapping class groups, Expert Systems with Applications, 34(1), 2008, pp. 673–682. Pal N., Bezdek J.; On cluster validity for the fuzzy c-means model, IEEE Transactions on Fuzzy Systems, 3(3), 1995, pp. 370–379. Brodowski S.; A Validity Criterion for Fuzzy Clustering, in: Jedrzejowicz P., Nguyen N. T., Hoang K. (ed.), Computational Collective Integlligence – ICCCI 2011, Springer, Berlin, Heidelberg, 2011. Bielecki A., Bielecka M., Chmielowiec A.; Input Signals Normalization in Kohonen Neural Networks, Lecture Notes in Artificial Intelligence, 5097, 2008, pp. 3–10. Barszcz T., Bielecka M., Bielecki A., Wojcik M.; Wind turbines states classification by a fuzzy-ART neural network with a stereographic projection as a signal normalization, Lecture Notes in Computer Science, 6594, 2011, pp. 225–234. Brodowski S.; Adaptujacy sie hierarchiczny aproksymator, Master's thesis, Jagiellonian University, 2007.