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ICANN ’94 pp 735-738 | Cite as

Task Decomposition and Correlations in Growing Artificial Neural Networks

  • Jan Matti Langet
  • Hans-Michael Voigt
  • Dietrich Wolft
Conference paper

Abstract

To reduce the engineering efforts for the design of neural network architectures a data driven algorithm is desirable which constructs a network during the learning process. For structure adaptation different approaches with evolutionary algorithms (Voigt et. al., 1993), growth algorithms (Fahlmann et. al., 1990), and others are used.

Keywords

Hide Layer Residual Error Input Space Window Function 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. [S.E. Fahlman & C. Lebiere, 1990] “The Cascade-Correlation Learning Architecture”, in Touretzky (ed.) Advances in Neural Information Processing Systems 2, Morgan-KaufmannGoogle Scholar
  2. [M. Jordan & R. A. Jacobs, 1992] “Hierarchies of adaptive experts”, in Proceedings of 1992 Neural Information Processing Systems Conference, vol. 4, pp. 985–992Google Scholar
  3. [S. Lee & R.M. Kil, 1991] “A Gaussian Potential Function Network With Hierarchically Self-Organizing Learning”, in Neural Networks, vol. 4, pp. 207–224CrossRefGoogle Scholar
  4. [E. Littman & H. Ritter, 1993] “Generalization Abilities of Cascade Network Architecture”, in Artificial Neural Networks 5Google Scholar
  5. [F. Smieja & H. Mühlenbein, 1992] Reflective modular neural network systems, GMD Technical Report 633, Sankt Augustin, GermanyGoogle Scholar
  6. [H.-M. Voigt, J. Born & I.Santibanez-Koref, 1993] Evolutionary Structuring of Artificial Neural Networks, Technical University Berlin, Bionics and Evolution Techniques Lab, Technical Report TR-93–002Google Scholar

Copyright information

© Springer-Verlag London Limited 1994

Authors and Affiliations

  • Jan Matti Langet
    • 1
  • Hans-Michael Voigt
    • 1
    • 2
  • Dietrich Wolft
    • 1
  1. 1.Center for Applied Computer Science (GFaI)Germany
  2. 2.Bionics & Evolution TechniquesTechnical University BerlinBerlinGermany

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