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Improving prediction models applied in systems monitoring natural hazards and machinery Cover

Improving prediction models applied in systems monitoring natural hazards and machinery

International Journal of Applied Mathematics and Computer Science
Volume 22 (2012): Issue 2 (June 2012)
By:  and    
Open Access
|Jun 2012
References

References

  1. Bloedorn, E. and Michalski, R. (2002). Data-driven constructive induction,(2): 30-37.
    Open DOISearch in Google Scholar
  2. Boser, B., Guyon, I. and Vapnik, V. (1992). A training algorithm for optimal margin classifiers,, pp. 144-152.
    Search in Google Scholar
  3. Box, G. and Jenkins, G. (1994)., Prentice-Hall, Upper Saddle River, NJ.
    Search in Google Scholar
  4. Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1994)., Wadsworth, Belmont, CA.
    Search in Google Scholar
  5. Brockwell, P. and Davis, R. (2002)., Springer-Verlag, New York, NY.
    Open DOISearch in Google Scholar
  6. Broyden, C. (1969). A new double-rank minimization algorithm,: 670.
    Search in Google Scholar
  7. Cao, L. and Tay, F. (2003). Support vector machine with adaptive parameters in financial time series forecasting,(6): 1506-1518.
    Open DOISearch in Google Scholar
  8. Chen, X., Yang, J. and Liang, J. (2011). A flexible support vector machine for regression,, DOI 10.1007/s00521-011-0623-5.
    Search in Google Scholar
  9. Chunshien, L. and Kuo-Hsiang, C. (2007). Recurrent neuro-fuzzy hybrid-learning approach to accurate systems modeling,(2): 194-212.
    Open DOISearch in Google Scholar
  10. Czogała, E. and Łęski, J. (2000)., Springer-Verlag, New York, NY.
    Open DOISearch in Google Scholar
  11. Dembczyński, K., Kotowiski, W. and Słowiński, R. (2010). Ender: A statistical framework for boosting decision rules,(1): 52-90.
    Open DOISearch in Google Scholar
  12. Dixon, W. (1992)., Ph.D. thesis, University of Nottingham, Nottingham.
    Search in Google Scholar
  13. Duch, W., Adamczak, R. and Grabczewski, K. (2000). A new methodology of extraction, optimization and application of crisp and fuzzy logical rules,(10): 1-31.
    Search in Google Scholar
  14. Friedman, J., Kohavi, R. and Yun, Y. (1996). Lazy decision trees,, pp. 717-724.
    Search in Google Scholar
  15. Gale, W., Heasley, K., Iannacchione, A., Swanson, P., Hatherly, P. and King, A. (2001). Rock damage characterization from microseismic monitoring,, pp. 1313-1320.
    Search in Google Scholar
  16. Goldberg, D. (1989)., Addison-Wesley Publishing Company, Boston, MA.
    Search in Google Scholar
  17. Góra, G. and Wojna, A. (2002). Riona: A new classification system combining rule induction and instance-based learning,(4): 369-390.
    Search in Google Scholar
  18. Grychowski, T. (2008). Hazard assessment based on fuzzy logic,(4): 595-602.
    Search in Google Scholar
  19. Hao, P. (2010). New support vector algorithms with parametric insensitive/margin model,(1): 60-73.
    Open DOISearch in Google Scholar
  20. Jang, J.-S. (1994). Structure determination in fuzzy modelling: A fuzzy cart approach,, pp. 480-485.
    Search in Google Scholar
  21. Janssen, F. and Fürnkranz, J. (2010a). On the quest for optimal rule learning heuristics,(3): 343-379.
    Open DOISearch in Google Scholar
  22. Janssen, F. and Fürnkranz, J. (2010b). Separate-and-conquer regression,, pp. 81-89.
    Search in Google Scholar
  23. Jonak, J. (2002). Hazard assessment based on fuzzy logic,(3): 270-277.
    Open DOISearch in Google Scholar
  24. Kabiesz, J. (2005). Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks,(5): 1131-1147.
    Open DOISearch in Google Scholar
  25. Katayama, N. and Satoh, S. (1997). The SR-tree: An index structure for high dimensional nearest neighbor queries,, pp. 369-380.
    Search in Google Scholar
  26. Macleod, J., Luk, A. and Titterington, D. (1987). A reexamination of the distance-weighted k-nearest-neighbor classification rule,(4): 689-696.
    Open DOISearch in Google Scholar
  27. Malerba, D., Esposito, F., Ceci, M. and Appice, A. (2005). Topdown induction of model trees with regression and splitting nodes,(5): 612-625.
    Open DOISearch in Google Scholar
  28. Michalak, M. (2011). Adaptive kernel approach to the time series prediction,(3): 283-293.
    Open DOISearch in Google Scholar
  29. Nelles, O., Fink, A., Babuška, R. and Setnes, M. (2000). Comparison of two construction algorithms for Takagi-Sugeno fuzzy models,(4): 835-855.
    Search in Google Scholar
  30. Oh, S. and Pedrycz, W. (2000). Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems,(2): 205-230.
    Open DOISearch in Google Scholar
  31. Quinlan, J. (1992a). Learning with continuous classes,, pp. 343-348.
    Search in Google Scholar
  32. Quinlan, J. R. (1992b)., Morgan Kaufman Publishers, San Mateo, CA.
    Search in Google Scholar
  33. Quinlan, J. (1993). Combining instance-based learning and model-based learning,, pp. 236-243.
    Search in Google Scholar
  34. Rutkowski, L. (2004). Generalized regression neural networks in time-varying environment,(3): 576-596.
    Open DOISearch in Google Scholar
  35. Scholkopf, B., Smola, A., Williamson, R. and Bartlett, P. (2000). New support vector algorithms,(5): 1207-1245.
    Open DOISearch in Google Scholar
  36. Schuster, H. (1998)., VCH Verlagsgesellschaft, New York, NY.
    Search in Google Scholar
  37. Sikora, M. and Krzykawski, D. (2005). Application of data exploration methods in analysis of carbon dioxide emission in hard-coal mines dewater pump stations,(6): 57-67, (in Polish).
    Search in Google Scholar
  38. 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,(4): 493-505.
    Open DOISearch in Google Scholar
  39. Sikora, M. and Sikora, B. (2006). Application of machine learning for prediction a methane concentration in a coal mine,(4): 475-492.
    Search in Google Scholar
  40. Sikora, M. and Wróbel, Ł. (2010). Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines,(1): 91-114.
    Search in Google Scholar
  41. Siwek, K., Osowski, S. and Szupiluk, R. (2009). Ensemble neural network approach for accurate load forecasting in a power system,(2): 303-315, DOI: 10.2478/v10006-009-0026-2.
    Open DOISearch in Google Scholar
  42. Tay, F. and Cao, L. (2002). Modified support vector machines in financial time series forecasting,(1): 847-861.
    Open DOISearch in Google Scholar
  43. Taylor, J. and Cristianini, N. (2004)., Cambridge University Press, Cambridge.
    Search in Google Scholar
  44. Tong, H. (1990)., Oxford University Press, Oxford.
    Search in Google Scholar
  45. Torgo, L. (1997). Kernel regression trees,, pp. 118-127.
    Search in Google Scholar
  46. Vapnik, V. (1995)., Springer, New York, NY.
    Open DOISearch in Google Scholar
  47. Wang, Y. and Witten, I. (1997). Inducing model trees for continuous classes,, pp. 128-137.
    Search in Google Scholar
  48. Weigend, A., Huberman, B. and Rumelhart, D. (1990). Predicting the future: A connectionist approach,(3): 193-209.
    Open DOISearch in Google Scholar
  49. Wess, S., Althoff, K. and Derwand, G. (1994). Using k-d trees to improve the retrieval step in case-based reasoning,S. Wess, K.-D. Althoff and M. Richter (Eds.),, Springer-Verlag, Berlin, pp. 167-181.
    Open DOISearch in Google Scholar
  50. Wettschereck, D., Aha, D. and Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms,(1-5): 273-314.
    Open DOISearch in Google Scholar
  51. Wilson, D. and Martinez, T. R. (2000). An integrated instance-based learning algorithm,(1): 1-28.
    Open DOISearch in Google Scholar
  52. Witten, I. and Frank, E. (2005)., Morgan Kaufmann, San Francisco, CA.
    Search in Google Scholar
  53. Wnek, J. and Michalski, R. S. (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments,(2): 139-168.
    Open DOISearch in Google Scholar
  54. Yager, R. and Filev, D. (1994)., John Wiley and Sons, New York, NY.
    Search in Google Scholar
DOI: https://doi.org/10.2478/v10006-012-0036-3 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 477 - 491
Published on: Jun 28, 2012
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
natural hazards monitoring,
regression rules,
time series forecasting,
κ-nearest neighbors
Related subjects:
Mathematics,
Applied mathematics

© 2012 Marek Sikora, Beata Sikora, published by University of Zielona Góra
This work is licensed under the Creative Commons License.

Volume 22 (2012): Issue 2 (June 2012)
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