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Variational Principles in Pattern Theory

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Neural and Synergetic Computers

Part of the book series: Springer Series in Synergetics ((SSSYN,volume 42))

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Abstract

The understanding of pattern formation and its dual, pattern recognition, is one of the most exciting areas of present research. It is the question of how complex systems can generate coherent global structures and how systems are designed which, by means of sensory and perceptional mechanisms, can construct internal representations of patterns in the outside world. The field represents a remarkable confluence of several different strands of thought.

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References

  1. D. Amit, H. Gutfreund and H. Sompolinsky, Phys. Rev. A 32 (1985), 1007

    Article  MathSciNet  ADS  Google Scholar 

  2. E. R. Caianiello, Neural Networks, Springer 1968; and in Progress in Quantum Field Theory, H. Ezawa and S. Kamefuchi (eds.), North Holland 1986

    Google Scholar 

  3. V. Cerny, J. Optimization Theory and Appl. 45 (1985), 41

    Article  MathSciNet  MATH  Google Scholar 

  4. G. Dangelmayr and W. Güttinger, Geophys. J. R. Astr. Soc. 71 (1982), 79

    Article  MATH  Google Scholar 

  5. R. Eckmiller and Ch. v.d. Malsburg (eds.), Neural Computers, Springer 1988

    Google Scholar 

  6. K. S. Fu, Sequential Methods in Pattern Recognition and Machine Learning, Academic Press 1968

    Google Scholar 

  7. S. Geman and D. Geman, IEEE Trans. PAMI 6 (1984), 721

    Article  MATH  Google Scholar 

  8. S. Geman and C.-R. Hwang, SIAM J. Contr. and Opt. 24 (1986), 1031

    Article  MathSciNet  MATH  Google Scholar 

  9. W. Güttinger and G. Dangelmayr (eds.), The Physics of Structure Formation, Springer 1987

    Google Scholar 

  10. H. Haken, Z. Phys. B 61 (1985), 329; B 61 (1985), 335

    Article  MathSciNet  Google Scholar 

  11. H. Haken, in Physics of Cognitive Processes, E. R. Caianiello (ed.), World Scientific 1987

    Google Scholar 

  12. H. Haken, in Computational Systems - Natural and Artificial, H. Haken (ed.), p.2, Springer 1987

    Google Scholar 

  13. J. J. Hopfield, Proc. Natl. Acad. Sci. USA 79 (1982), 2554

    Article  MathSciNet  ADS  Google Scholar 

  14. J. J. Hopfield, Proc. Natl. Acad. Sci. USA 81 (1984), 3088

    Article  ADS  Google Scholar 

  15. S. Kirkpatrick, J. Stat. Phys. 34 (1984), 975

    Article  MathSciNet  ADS  Google Scholar 

  16. W. A. Little, Math. Biosci. 19 (1974), 101

    Article  MATH  Google Scholar 

  17. J. L. Lumley, Stochastic Tools in Turbulence, Academic Press 1970

    Google Scholar 

  18. M. Z. Nashed (ed.), Generalized Inverses and Applications, Academic Press 1976

    Google Scholar 

  19. C. Peterson and J. R. Anderson, Complex Systems 1 (1987), 995; 2 (1988), 59

    MathSciNet  MATH  Google Scholar 

  20. T. Poggio and C. Koch, Proc. R. Soc. Lond. B 226 (1985), 303

    Article  ADS  MATH  Google Scholar 

  21. T. Poggio and V. Torre, Artif. Intelligence Lab. Memo., no. 773, M.I.T. Cambridge, Massachusetts

    Google Scholar 

  22. L. Sirovich, in Proc. Int. Conf. Fluid Mech., Beijing 1987

    Google Scholar 

  23. D. J. Thouless, P. W. Anderson and R. G. Palmer, Phil. Mag. 35 (1977), 593

    Article  ADS  Google Scholar 

  24. A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-Posed Problems, Winston and Sons 1977

    Google Scholar 

  25. S. Watanabe, Pattern Recognition: Human and Mechanical, Wiley 1985

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Information Sciences, University of Tübingen, D-7400, Tübingen, Fed. Rep. of Germany

    W. Güttinger & G. Dangelmayr

Authors
  1. W. Güttinger
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  2. G. Dangelmayr
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Editor information

Editors and Affiliations

  1. Institut für Theoretische Physik und Synergetik, Universität Stuttgart, Pfaffenwaldring 57/IV, D-7000, Stuttgart 80, Fed. Rep. of Germany

    Hermann Haken

  2. Center for Complex Systems, Florida Atlantic University, 33431, Boca Raton, FL, USA

    Hermann Haken

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© 1988 Springer-Verlag Berlin Heidelberg

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Güttinger, W., Dangelmayr, G. (1988). Variational Principles in Pattern Theory. In: Haken, H. (eds) Neural and Synergetic Computers. Springer Series in Synergetics, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-74119-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-74119-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-74121-0

  • Online ISBN: 978-3-642-74119-7

  • eBook Packages: Springer Book Archive

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