References
- Bloedorn, E. and Michalski, R. (2002). Data-driven constructive induction, IEEE Intelligent Systems 13(2): 30-37.
- Boser, B., Guyon, I. and Vapnik, V. (1992). A training algorithm for optimal margin classifiers, Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, USA, pp. 144-152.
- Box, G. and Jenkins, G. (1994). Time Series Analysis: Forecasting and Control, Prentice-Hall, Upper Saddle River, NJ.
- Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1994). Classification and Regression Trees, Wadsworth, Belmont, CA.
- Brockwell, P. and Davis, R. (2002). Introduction to Time Series Forecasting, Springer-Verlag, New York, NY.
- Broyden, C. (1969). A new double-rank minimization algorithm, Notices of the American Mathematical Society 16: 670.
- Cao, L. and Tay, F. (2003). Support vector machine with adaptive parameters in financial time series forecasting, IEEE Transactions on Neural Networks 14(6): 1506-1518.
- Chen, X., Yang, J. and Liang, J. (2011). A flexible support vector machine for regression, Neural Computing & Applications, DOI 10.1007/s00521-011-0623-5.
- Chunshien, L. and Kuo-Hsiang, C. (2007). Recurrent neuro-fuzzy hybrid-learning approach to accurate systems modeling, Fuzzy Sets and Systems 158(2): 194-212.
- Czogała, E. and Łęski, J. (2000). Fuzzy and Neuro-Fuzzy Intelligent Systems. Studies in Fuzziness and Soft Computing, Springer-Verlag, New York, NY.
- Dembczyński, K., Kotowiski, W. and Słowiński, R. (2010). Ender: A statistical framework for boosting decision rules, Data Mining and Knowledge Discovery 21(1): 52-90.
- Dixon, W. (1992). A Statistical Analysis of Monitored Data for Methane Prediction, Ph.D. thesis, University of Nottingham, Nottingham.
- Duch, W., Adamczak, R. and Grabczewski, K. (2000). A new methodology of extraction, optimization and application of crisp and fuzzy logical rules, IEEE Transactions on Neural Networks 11(10): 1-31.
- Friedman, J., Kohavi, R. and Yun, Y. (1996). Lazy decision trees, Proceedings of AAAI/IAAI, Portland, OR, USA, pp. 717-724.
- Gale, W., Heasley, K., Iannacchione, A., Swanson, P., Hatherly, P. and King, A. (2001). Rock damage characterization from microseismic monitoring, Proceedings of the 38th US Symposium of Rock Mechanics, Lisse, The Netherlands, pp. 1313-1320.
- Goldberg, D. (1989). Genetics Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, Boston, MA.
- Góra, G. and Wojna, A. (2002). Riona: A new classification system combining rule induction and instance-based learning, Fundamenta Informaticae 51(4): 369-390.
- Grychowski, T. (2008). Hazard assessment based on fuzzy logic, Archives of Mining Sciences 53(4): 595-602.
- Hao, P. (2010). New support vector algorithms with parametric insensitive/margin model, Neural Networks 23(1): 60-73.
- Jang, J.-S. (1994). Structure determination in fuzzy modelling: A fuzzy cart approach, Proceedings of the IEEE International Conference on Fuzzy Systems, Orlando, FL, USA, pp. 480-485.
- Janssen, F. and Fürnkranz, J. (2010a). On the quest for optimal rule learning heuristics, Machine Learning 78(3): 343-379.
- Janssen, F. and Fürnkranz, J. (2010b). Separate-and-conquer regression, Proceedings of LWA 2010: Lernen, Wissen, Adaptivität, Kassel, Germany, pp. 81-89.
- Jonak, J. (2002). Hazard assessment based on fuzzy logic, Journal of Mining Sciences 38(3): 270-277.
- Kabiesz, J. (2005). Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks, Geotechnical and Geological Engineering 24(5): 1131-1147.
- Katayama, N. and Satoh, S. (1997). The SR-tree: An index structure for high dimensional nearest neighbor queries, Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, pp. 369-380.
- Macleod, J., Luk, A. and Titterington, D. (1987). A reexamination of the distance-weighted k-nearest-neighbor classification rule, IEEE Transactions on Systems, Man and Cybernetics 17(4): 689-696.
- Malerba, D., Esposito, F., Ceci, M. and Appice, A. (2005). Topdown induction of model trees with regression and splitting nodes, IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5): 612-625.
- Michalak, M. (2011). Adaptive kernel approach to the time series prediction, Pattern Analysis and Applications 14(3): 283-293.
- Nelles, O., Fink, A., Babuška, R. and Setnes, M. (2000). Comparison of two construction algorithms for Takagi-Sugeno fuzzy models, International Journal of Applied Mathematics and Computer Science 10(4): 835-855.
- Oh, S. and Pedrycz, W. (2000). Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems, Fuzzy Sets and Systems 115(2): 205-230.
- Quinlan, J. (1992a). Learning with continuous classes, Proceedings of the International Conference on Artificial Intelligence, Singapore, pp. 343-348.
- Quinlan, J. R. (1992b). C4.5 Programs for Machine Learning, Morgan Kaufman Publishers, San Mateo, CA.
- Quinlan, J. (1993). Combining instance-based learning and model-based learning, Proceedings of the 10th International Conference on Machine Learning, San Mateo, CA, USA, pp. 236-243.
- Rutkowski, L. (2004). Generalized regression neural networks in time-varying environment, IEEE Transactions on Neural Networks 15(3): 576-596.
- Scholkopf, B., Smola, A., Williamson, R. and Bartlett, P. (2000). New support vector algorithms, Neural Computation 12(5): 1207-1245.
- Schuster, H. (1998). Deterministic Chaos, VCH Verlagsgesellschaft, New York, NY.
- Sikora, M. and Krzykawski, D. (2005). Application of data exploration methods in analysis of carbon dioxide emission in hard-coal mines dewater pump stations, Mechanizacja i Automatyzacja Górnictwa 413(6): 57-67, (in Polish).
- Sikora, M., Krzystanek, Z., Bojko, B. and Śpiechowicz, K. (2011). Application of a hybrid method of machine learning for description and on-line estimation of methane hazard in mine workings, Journal of Mining Sciences 47(4): 493-505.
- Sikora, M. and Sikora, B. (2006). Application of machine learning for prediction a methane concentration in a coal mine, Archives of Mining Sciences 51(4): 475-492.
- Sikora, M. and Wróbel, Ł. (2010). Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines, Archives of Mining Sciences 55(1): 91-114.
- Siwek, K., Osowski, S. and Szupiluk, R. (2009). Ensemble neural network approach for accurate load forecasting in a power system, International Journal of Applied Mathematics and Computer Science 19(2): 303-315, DOI: 10.2478/v10006-009-0026-2.
- Tay, F. and Cao, L. (2002). Modified support vector machines in financial time series forecasting, Neurocomputing 48(1): 847-861.
- Taylor, J. and Cristianini, N. (2004). Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge.
- Tong, H. (1990). Non-linear Time Series: A Dynamical Systems Approach, Oxford University Press, Oxford.
- Torgo, L. (1997). Kernel regression trees, Proceedings of Poster Papers, European Conference on Machine Learning, Prague, Czech Republic, pp. 118-127.
- Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer, New York, NY.
- Wang, Y. and Witten, I. (1997). Inducing model trees for continuous classes, Proceedings of Poster Papers, European Conference on Machine Learning, Prague, Czech Republic, pp. 128-137.
- Weigend, A., Huberman, B. and Rumelhart, D. (1990). Predicting the future: A connectionist approach, International Journal of Neural Systems 1(3): 193-209.
- Wess, S., Althoff, K. and Derwand, G. (1994). Using k-d trees to improve the retrieval step in case-based reasoning, in S. Wess, K.-D. Althoff and M. Richter (Eds.), Topics in Case-Based Reasoning, Springer-Verlag, Berlin, pp. 167-181.
- Wettschereck, D., Aha, D. and Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms, Artificial Intelligence Review 11(1-5): 273-314.
- Wilson, D. and Martinez, T. R. (2000). An integrated instance-based learning algorithm, Computational Intelligence 16(1): 1-28.
- Witten, I. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, CA.
- Wnek, J. and Michalski, R. S. (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments, Machine Learning 14(2): 139-168.
- Yager, R. and Filev, D. (1994). Essentials of Fuzzy Modeling and Control, John Wiley and Sons, New York, NY.