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Classification with neural networks

  • A. Müller
  • J. Neumann
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In the fields of artificial intelligence, cognitive psychology, neurophysiology, and informatics in recent times neural networks have received a great deal of attention. Some general properties of these systems are discussed and exemplified in applications. The models used are a HOPFIELD-network and the BACKPROPAGATION learning algorithm. The latter is applied in the otological classification of persons regarding evoked otoacoustic emissions of normal or diseased ears, resp. The results show, that up to 71.1% are correctly classified. Classificatory abilities of neural networks, problems of preprocessing of spectral data and their analysis by backpropagation are discussed. Finally, there will be a short comparison between (higher order) associative memories and discriminant analysis.

Keywords

Neural Network Test Pattern Sensorineural Hearing Loss Associative Memory Hide Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Ackley, D.H., Hinton, G.E., Sejnowski, T.J. (1985): A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147–169.Google Scholar
  2. Amaei, S.I., Maginu, K. (1988): Statistical neurodynamics of associative memory. Neural Networks, 1, 63–73.Google Scholar
  3. Amit, D.J., Gutpreund, H.& Sompolinsky, H. (1985): Storing infinite numbers of patterns in a spin-glas model of neuronal networks. Phys. Rev. Letters, 55, 1530–1533.Google Scholar
  4. Bock, H.H. (1988) (ED.): Classification and related methods of data analysis. Elsevier, Amsterdam.zbMATHGoogle Scholar
  5. Choi, M.Y., Hubermann, B.A. (1984): Nature of time in Monte Carlo processes. Phys. Rev., 29, 2796–2798.Google Scholar
  6. Dammasch, I.E., Wolff, J.R. (1989): Morphological realization of associative memory. In: N. Elsner, W. Singer (Eds.): Dynamics and plasticity in neuronal Systems. Proceedings of the 17th Göttinger Neurobiology Conference. Thieme, Stuttgart.Google Scholar
  7. Van Dijk, P. Witt, H.P., Segenhout, J.M. (1989): Spontaneous otoacoustic emissions in the European edible frog (Rana esculenta): Spectral details and temperature dependence. Hearing Research, 42, 273–282.Google Scholar
  8. Gallant, S.I. (1986): Optimal linear discriminants. IEEE Proceedings of the 8th International Conference on Pattern Recognition. IEEE Computer Society, Washington.Google Scholar
  9. Geman, S., Geman, D. (1984): Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.CrossRefzbMATHGoogle Scholar
  10. Gorman, R.P., Sejnowski, T.J. (1988): Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks, 1, 75–89.Google Scholar
  11. Grondin, R.O., Porod, W., LOEFFLER, CM., Ferry, D.G. (1983): Synchronous and asynchronous systems of threshold elements. Biol. Cybernetics, 49, 1–7.zbMATHGoogle Scholar
  12. Grossberg, S. (1988): Nonlinear neural networks: principles, mechanisms, and architectures. Neural Networks, 1, 17–61.Google Scholar
  13. Hebb, D.O. (1949): The organization of behavior. Wiley, New York.Google Scholar
  14. Hopfield, J.J. (1982): Neuronal networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences (USA), 79, 2554–2558.CrossRefMathSciNetGoogle Scholar
  15. Kemp, D.T. (1978): Stimulated acoustic emissions from within the human auditory system. Journal of the Acoustic Society, Am. 64, 1386–1391.CrossRefGoogle Scholar
  16. Kinzel, W. (1985): Learning and pattern recognition in spin glass models. Zeitschrift f. Physik Condensed Matter, 60, 205–213.CrossRefGoogle Scholar
  17. Kirkpatrick, S., Gelatt, D.D., Vecchi, M.P. (1983): Optimization by simulated annealing. Science, 220, 671–680.zbMATHMathSciNetGoogle Scholar
  18. Kohonen, T. (1987): Content-addressable memories (2nd ed). Springer, Berlin.Google Scholar
  19. Kohonen, T. (1988A): Self-organization and associative memory (2nd ed.). Springer, Berlin.Google Scholar
  20. Kohonen, T. (1988B): An introduction to neural computing. Neural Networks, 1, 3–16.CrossRefGoogle Scholar
  21. Kree, R., Zippelius, A. (1988): A recognition of topological features of graphs and images in neural networks. Journal of Physics, A 21, L 813.Google Scholar
  22. Mccullough, W.S., Pitts, W. (1943): logical calculus of the ideas immanant in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.CrossRefGoogle Scholar
  23. Von der Malsburg, C. (1988): Pattern recognition by labeled graph matching. Neural Networks, 1, 141–148.Google Scholar
  24. Minsky, M., Papert, S. (1969): Perceptrons. MIT Press, Cambridge MA.zbMATHGoogle Scholar
  25. Müller, A. (1990): Neural networks in optimization — a generalization of SIGH. IWSP research report, University of Göttingen.Google Scholar
  26. Müller, A., Kadach, J. (1990): Pfadmodelle und (künstliche) neuronale Netzwerke: gibt es Konvergenzen? In: SEIDEL (Hrsg.): Beiträge zur X. Tagung der Arbeitsgruppe Strukturgleichungsmodelle, Berlin, 1990.Google Scholar
  27. Psaltis, D., Park, C.H., Hong, J. (1988): Higher order associative memories and their optical implementations. Neural Networks, 2, 149–163.CrossRefGoogle Scholar
  28. Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1988): Learning internal representations by error propagation. In: Rumelhart, D. E., Mcclelland, J. L. (Eds.): Parallel distributed processing: Explorations in the microstructure of cognition. MIT Press, Cambridge MA.Google Scholar
  29. Surkan, A.J. (1988): Neural net connection estimates applied for feature selection and improved linear classifier design. In: Bouchon, B., Yager, R.R. (Eds.): Uncertainty and intelligent systems. Springer, New York.Google Scholar
  30. Widrow, G. (1962): Generalization and information storage in networks of Adaline neurons. In: Yovits, M. C., Jacobi, G. T., Goldstein, G. D. (eds.): Self-organizing systems. Spartan Books, Washington DC.Google Scholar
  31. Widrow, G., Hoff, M.E. (1960): Adaptive switching circuits. Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record, Part 4, 96–104.Google Scholar
  32. Zwicker, E. (1985): Das Innenohr als aktives schallverarbeitendes und schallaussendendes System. In: Fortschritte der Akustik — DAGA 1985, 29–44, Bad Honnef: DPG Kongress GmbH.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1991

Authors and Affiliations

  • A. Müller
    • 1
    • 2
  • J. Neumann
    • 1
    • 2
  1. 1.Institut für Wirtschafts- und SozialpsychologieUniversität GöttingenGöttingenGermany
  2. 2.III. Physikalisches InstitutUniversität GöttingenGöttingenGermany

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