Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems

  • Juan M. Corchado
  • Emilio S. Corchado
  • Jim Aiken
  • Colin Fyfe
  • Florentino Fernandez
  • Manuel Gonzalez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2689)

Abstract

CBR systems are normally used to assist experts in the resolution of problems. During the last few years researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a Maximum Likelihood Hebbian Learning-based method that automates the organisation of cases and the retrieval stage of casebased reasoning systems. The proposed methodology has been derived as an extension of the Principal Component Analysis, and groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.

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References

  1. 1.
    Aamodt A. and Plaza E. (1994) Case-Based Reasoning: foundational Issues, Methodological Variations, and System Approaches. AICOM. Vol. 7. No 1. March 1994.Google Scholar
  2. 2.
    Corchado E. and Fyfe C. (2002) Maximum and Minimum Likelihood Hebbian Rules as a Exploratory Method. 9th International Conference on Neural Information Processing. 18–22 November 2002. Singapore.Google Scholar
  3. 3.
    Corchado E., MacDonald D. and Fyfe C. (2002) Optimal Projections of High Dimensional Data. ICDM’ 02. The 2002 IEEE International Conference on Data Mining, IEEE Computer Society; Maebashi TERRSA, Maebashi City, Japan December 9–12, 2002Google Scholar
  4. 4.
    Corchado J. M. and Aiken J. (2003) Hybrid Artificial Intelligence Methods in Oceanographic Forecasting Models. IEEE SMC Transactions Part C. Vol.32, No.4, November 2002: 307–313.Google Scholar
  5. 5.
    Corchado J. M. and Lees B. (2000) Adaptation of cases for case-based forecasting with neural network support., S.K Pal, T.S. Dillon and D.S. Yeung (Eds.), "Soft Computing in Case Based Reasoning", Springer Verlag, London.Google Scholar
  6. 6.
    Corchado J. M., Aiken J. and Rees N. (2001) Artificial Intelligence Models for Oceanographic Forecasting. Plymouth Marine Laboratory, U.K, 2001. ISBN: 0-9519618-4-5.Google Scholar
  7. 7.
    Diaconis P. and Freedman D. (1984) Asymptotics of Graphical Projections. The Annals of Statistics. 12(3): 793–815.MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Fdez-Riverola F. and Corchado J. M. (2003) FSfRT: Forecasting System for Red Tides. Applied Intelligence. Soft Computing in Case-Based Reasoning. In press. ISSN: 0924-669X.Google Scholar
  9. 9.
    Fyfe C. and Baddeley R. ( 1995) Non-linear data structure extraction using simple Hebbian networks, Biological Cybernetics 72(6), p533–541.MATHCrossRefGoogle Scholar
  10. 10.
    Fyfe C. and Corchado J. M. (2001) Automating the construction of CBR Systems using Kernel Methods. International Journal of Intelligent Systems. Vol 16, No. 4, April 2001. ISSN 0884-8173.Google Scholar
  11. 11.
    Fyfe C. and MacDonald D. (2001) ɛ Insensitive Hebbian learning, Neuro Computing.Google Scholar
  12. 12.
    Fyfe C. and Corchado E. (2002a). Maximum Likelihood Hebbian Rules. 10th European Symposium on Artificial Neural Networks, ESANN’2002, Bruges, April 24–25–26, 2002.Google Scholar
  13. 13.
    Fyfe C. and Corchado E., (2002b) A New Neural Implementation of Exploratory Projection Pursuit. IDEAL2002 Third International Conference on Intelligent Data Engineering and Automated Learning. Manchester, UK. 12–14 August, 2002.Google Scholar
  14. 14.
    Hyvärinen A. (2001) Complexity Pursuit: Separating interesting components from time series. Neural Computation, 13: 883–898.MATHCrossRefGoogle Scholar
  15. 15.
    Hyvärinen A. Karhunen J. and Oja E. (2002) Independent Component Analysis, Wiley, ISBN 0-471-40540-X.Google Scholar
  16. 16.
    Karhunen J. and Joutsensalo J. ( 1994.) Representation and Separation of Signals Using Non-linear PCA Type Learning, Neural Networks, 7:113–127.CrossRefGoogle Scholar
  17. 17.
    Kashyap R. L., Blaydon C. C., and Fu K. S. (1994.) Stochastic Approximation. in A Prelude to Neural Networks: Adaptive and Learning Systems, Ed Jerry M. Mendel, Prentice Hall, ISBN 0-13-147448-0.Google Scholar
  18. 18.
    Lai P. L., Charles D., and Fyfe C., (2000.) Seeking Independence using Biologically Inspired Artificial Neural Networks, in Developments in Artificial Neural Network Theory: Independent Component Analysis and Blind Source Separation, Editor M. A. Girolami, Springer Verlag.Google Scholar
  19. 19.
    Lees B. and Corchado J. M. (1999) Integrated case-based approach to problem solving. In: Lecture Notes in Artificial Intelligence 1570, XPS-99: Knowledge-Based Systems-Survey and Future Directions, edited by Frank Puppe, Springer, Berlin, pp. 157–166.Google Scholar
  20. 20.
    MacDonald D. and Fyfe C. (2000) The Kernel self-organising map. In R.J Howlett and L.C. Jain, editors, Fourth International Conference on Knowledge-based Intelligent Engineering Sustems and Allied Technologies, KES 2000.Google Scholar
  21. 21.
    Oja E. ( 1989) Neural Networks, Principal Components and Subspaces, International Journal of Neural Systems, 1:61–68.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Juan M. Corchado
    • 1
  • Emilio S. Corchado
    • 4
  • Jim Aiken
    • 2
  • Colin Fyfe
    • 3
  • Florentino Fernandez
    • 5
  • Manuel Gonzalez
    • 1
  1. 1.Dept. InformáticaUniversity of VigoOurenseSpain
  2. 2.Dept. de Informática y AutomáticaUniversity of SalamancaSalamancaSpain
  3. 3.Dept. de Ingeniería CivilUniversity of BurgosBurgosSpain
  4. 4.Plymouth Marine LaboratoryPlymouthUK
  5. 5.Computing and Information System DeptUniversity of PaisleyPaisleyUK

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