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Do Smart Adaptive Systems Exist? — Introduction

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Do Smart Adaptive Systems Exist?

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 173))

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References

  1. A. Abraham, “Intelligent Systems: Architectures and Perspectives”, Recent Advances in Intelligent Paradigms and Applications, Abraham A., Jain L. and Kacprzyk J. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, Chap. 1, pp. 1–35, Aug. 2002

    Google Scholar 

  2. A. Abraham, “EvoNF: A Framework for Optimization of Fuzzy Inference Systems Using Neural Network Learning and Evolutionary Computation”, 2002 IEEE International Symposium on Intelligent Control (ISIC’02), Canada, IEEE Press, pp. 327–332, 2002

    Google Scholar 

  3. B. Azvine and W. Wobcke, “Human-centered Intelligent Systems and Soft Computing”, BT Technology Journal, vol. 16, no. 3, July 1998

    Google Scholar 

  4. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981

    Google Scholar 

  5. C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press: Oxford, 1995

    Google Scholar 

  6. P. Bonissone, Y-T. Chen, K. Goebel, P.S. Khedkar, “Hybrid Soft Computing Systems: Industrial and Commercial Applications”, Proc. Of the IEEE, vol. 87, no. 9, Sep. 1999

    Google Scholar 

  7. L. Breiman, “Bagging predictors”, Machine Learning, vol. 24, no. 2, pp. 123–140, 1996

    MATH  MathSciNet  Google Scholar 

  8. T.G. Dietterich, “Machine Learning Research: Four Current Directions”, AI Magazine, vol. 18, no. 4, pp. 97–136, 1997

    Google Scholar 

  9. T.G. Dietterich, “An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization”, Machine Learning, vol. 40, pp. 139–157, 2000

    Article  Google Scholar 

  10. C. Doom, “Get Smart: How Intelligent Technology Will Enhance Our World”, CSC’s Leading Edge Forum Report (www.csc.com), 2001

    Google Scholar 

  11. J. Doyle, T. Dean et al., “Strategic Directions in Artificial Intelligence”, ACM Computing Surveys, vol. 28, no. 4, Dec. 1996

    Google Scholar 

  12. H. Drucker, C. Cortes, L.D. Jackel, Y. LeCun, and V. Vapnik, “Boosting and other ensemble methods”, Neural Computation, vol. 6, pp. 1289–1301, 1994

    MATH  Google Scholar 

  13. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, John Wiley and Sons, Inc., 2000

    Google Scholar 

  14. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds., Advances in Knowledge Discovery and Data Mining, Menlo Park, CA: AAAI/MIT Press, 1996

    Google Scholar 

  15. U. Fayyad, G.P. Shapiro, and P. Smyth, “TheKDDprocess for extracting useful knowledge from volumes of data”, Commun. ACM, vol. 39, pp. 27–34, 1996

    Article  Google Scholar 

  16. D.B. Fogel, Evolving neural networks, Biol. Cybern., vol. 63, pp. 487–493, 1990

    Article  Google Scholar 

  17. B. Gabrys and L. Petrakieva, “Combining labelled and unlabelled data in the design of pattern classification systems”, International Journal of Approximate Reasoning, vol. 35, no. 3, pp. 251–273, 2004

    Article  MATH  MathSciNet  Google Scholar 

  18. B. Gabrys, “Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or not to Combine?”, Fuzzy Sets and Systems, vol. 147, pp. 39–56, 2004

    Article  MATH  MathSciNet  Google Scholar 

  19. B. Gabrys, “Neuro-Fuzzy Approach to Processing Inputs with Missing Values in Pattern Recognition Problems”, International Journal of Approximate Reasoning, vol. 30, no. 3, pp. 149–179, 2002

    Article  MATH  MathSciNet  Google Scholar 

  20. B. Gabrys, “Agglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network”, the special issue of the Journal of VLSI Signal Processing Systems entitled “Advances in Neural Networks for Signal Processing”, vol. 32, no. 1/2, pp. 67–82, 2002

    Article  MATH  Google Scholar 

  21. B. Gabrys, “Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability”, Proceedings of the Int. Joint Conference on Neural Networks, (IJCNN’2002) a part of the WCCI’2002 Congress, ISBN: 0-7803-7278-6, pp. 2410–2415, Honolulu, USA, May 2002

    Google Scholar 

  22. B. Gabrys and A. Bargiela, “General Fuzzy Min-Max Neural Network for Clustering and Classification”, IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 769–783, 2000

    Article  Google Scholar 

  23. B. Gabrys and A. Bargiela, “Neural Networks Based Decision Support in Presence of Uncertainties”, ASCE J. of Water Resources Planning and Management, vol. 125, no. 5, pp. 272–280, 1999

    Article  Google Scholar 

  24. D.E. Goldberg, Genetic Algorithms in Search, Optimization and machine Learning, Addison-Wesley, Reading, MA, 1989

    Google Scholar 

  25. S. Haykin, Neural Networks. A Comprehensive Foundation, Macmillan College Publishing Company, New York, 1994

    Google Scholar 

  26. J.H. Holland, Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI, 1975

    Google Scholar 

  27. N. Kasabov and R. Kozma (Eds.), Neuro-Fuzzy Techniques for Intelligent Information Systems, Physics Verlag, 1999

    Google Scholar 

  28. N. Kasabov, Evolving connectionist systems — methods and applications in bioinformatics, brain study and intelligent machines, Springer Verlag, London-New York, 2002

    Google Scholar 

  29. R. Khosla and T. Dillon, Engineering Intelligent Hybrid Multi-Agent Systems, Kluwer Academic Publishers, 1997

    Google Scholar 

  30. J.R. Koza, Genetic Programming, MIT Press, 1992

    Google Scholar 

  31. L.I. Kuncheva, Fuzzy Classifier Design, Physica-Verlag Heidelberg, 2000

    Google Scholar 

  32. C.T. Leondes (Ed.), Intelligent Systems: Technology and Applications (vol. 1 — Implementation Techniques; vol. 2 — Fuzzy Systems, Neural Networks, and Expert Systems; vol. 3 — Signal, Image and Speech Processing; vol. 4 — Database and Learning Systems; vol. 5 — Manufacturing, Industrial, and Management Systems; vol. 6 — Control and Electric Power Systems), CRC Press, 2002

    Google Scholar 

  33. L.R. Medsker, Hybrid Intelligent Systems, Kluwer Academic Publishers, 1995

    Google Scholar 

  34. S. Mitra and Y. Hayashi, “Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework”, IEEE Trans. on Neural Networks, vol. 11, no. 3, pp. 748–768, May 2000

    Article  Google Scholar 

  35. T.M. Mitchell, “Machine learning and data mining”, Commun. ACM, vol. 42, no. 11, pp. 477–481, 1999

    Article  Google Scholar 

  36. S. Mitra, S.K. Pal and P. Mitra, “Data Mining in Soft Computing Framework: A Survey”, IEEE Trans. on Neural Networks, vol. 13, no. 1, pp. 3–14, 2002

    Article  MATH  Google Scholar 

  37. D. Nauck and R. Kruse, “A neuro-fuzzy method to learn fuzzy classification rules from data”, Fuzzy Sets and Systems, vol. 89, no. 3, pp. 277–288, 1997

    Article  MathSciNet  Google Scholar 

  38. D. Nauck and R. Kruse. “Neuro-fuzzy systems for function approximation”, Fuzzy Sets and Systems, vol. 101, pp. 261–271, 1999

    Article  MATH  MathSciNet  Google Scholar 

  39. S. Pal, V. Talwar, P. Mitra, “Web Mining in Soft Computing Framework: Relevance, State of the Art and Future Directions”, To appear in IEEE Trans. on Neural Networks, 2002

    Google Scholar 

  40. W. Pedrycz (Ed.), Fuzzy Evolutionary Computation, Kluwer Academic Publishers, USA, 1997

    Google Scholar 

  41. W. Pedrycz and F. Gomide, An Introduction to Fuzzy Sets: Analysis and Design, The MIT Press, 1998

    Google Scholar 

  42. L. Petrakieva and B. Gabrys, “Selective Sampling for Combined Learning from Labelled and Unlabelled Data”, In the book on “Applications and Science in Soft Computing” published in the Springer Series on Advances in Soft Computing, Lotfi, A. and Garibaldi, J.M. (Eds.), ISBN: 3-540-40856-8, pp. 139–148, 2004

    Google Scholar 

  43. D. Pyle, Data Preparation for Data Mining, Morgan Kaufman Publishers, 1999

    Google Scholar 

  44. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufman, San Mateo, CA, 1993

    Google Scholar 

  45. B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996

    Google Scholar 

  46. E.H. Ruspini, P.P. Bonissone, and W. Pedrycz (Eds.), Handbook of Fuzzy Computation, Institute of Physics, Bristol, UK, 1998

    Google Scholar 

  47. D. Ruta, and B. Gabrys, “An Overview of Classifier Fusion Methods”, Computing and Information Systems (Ed. Prof. M. Crowe), University of Paisley, vol. 7, no. 1, pp. 1–10, 2000

    Google Scholar 

  48. D. Ruta, and B. Gabrys, “Classifier Selection for Majority Voting”, Special issue of the journal of INFORMATION FUSION on Diversity in Multiple Classifier Systems, vol. 6/1 pp. 63–81, 2004

    Article  Google Scholar 

  49. R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, MIT Press, 1998

    Google Scholar 

  50. G. Tselentis and M. van Someren, “A Technological Roadmap for Computational Intelligence and Learning”, Available at www.dcs.napier.ac.uk/coilweb/, June 2000

    Google Scholar 

  51. V. Vapnik, The nature of statistical learning theory, Springer Verlag, New York, 1995

    Google Scholar 

  52. S.M. Weiss and C.A. Kulikowski, Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, Morgan Kaufman Publishers, Inc., 1991

    Google Scholar 

  53. X. Yao, “Evolving Artificial Neural Networks”, Proc. of the IEEE, vol. 87, no. 9, pp. 1423–47, Sep. 1999

    Article  Google Scholar 

  54. L. Zadeh, “Fuzzy sets”, Information and Control, vol. 8, pp. 338–353, 1965

    Article  MATH  MathSciNet  Google Scholar 

  55. L. Zadeh, “Fuzzy logic and the calculi of fuzzy rules and fuzzy graphs: A precis”. Int. J. Multiple-Valued Logic, vol. 1, pp. 1–38, 1996

    MATH  MathSciNet  Google Scholar 

  56. L. Zadeh, “Soft Computing and Fuzzy Logic”, IEEE Software, pp. 48–56, Nov. 1994

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Intelligence Research Group, School of Design, Engineering and Computing, Bournemouth University, Bournemouth

    B. Gabrys

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  1. B. Gabrys
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Editor information

Editors and Affiliations

  1. School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, BH12 5BB, UK

    Bogdan Gabrys

  2. Department of Process Engineering, Control Engineering Laboratory, University Oulu, P.O.Box 4300, 90014, Oulu, Finland

    Kauko Leiviskä

  3. Institut für Mechanik, Lehrstuhl für Technische Dynamik, Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Jens Strackeljan

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Gabrys, B. (2005). Do Smart Adaptive Systems Exist? — Introduction. In: Gabrys, B., Leiviskä, K., Strackeljan, J. (eds) Do Smart Adaptive Systems Exist?. Studies in Fuzziness and Soft Computing, vol 173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32374-0_1

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  • DOI: https://doi.org/10.1007/3-540-32374-0_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24077-8

  • Online ISBN: 978-3-540-32374-7

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