Advertisement

An agent-based operational model for hybrid connectionist-symbolic learning

  • José C. González
  • Juan R. Velasco
  • Carlos A. Iglesias
Artificial Neural Nets Simulation and Implementation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1607)

Abstract

Hybridization of connectionist and symbolic systems is being proposed for machine learning purposes in many applications for different fields. However, a unified framework to analyse and compare learning methods has not appeared yet. In this paper, a multiagent-based approach is presented as an adequate model for hybrid learning. This approach is built upon the concept of bias.

Keywords

Unify Framework Tactical Level Majority Vote Scheme Bias Management Search Bias 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    P. Chan and S. Stolfo. A comparative evaluation of voting and meta-learning on partitioned data. In Prieditis and Russell [6], Proceedings of the 11th International Conference on Machine Learning, Tahoe City, CA, 1995. Morgan Kaufmann, pages 90–98.Google Scholar
  2. 2.
    J. Gama and P. Brazdil. Characterization of classification algorithms. In E. Pinto-Ferreira and N. Mamede, editors, Progress in Artificial Intelligence. Proceedings of the 7th Portuguese Conference on Artificial Intelligence (EPIA-95), pages 189–200. Springer-Verlag, 1995.Google Scholar
  3. 3.
    Melanie Hilario. Bias and knowledge in symbolic and connectionist induction. Technical report, Centre Universitaire d'Informatique, Université de Genève, Genève, Switzerland, 1997.Google Scholar
  4. 4.
    R. Kohavi and G. John. Automatic parameter selection by minimizing estimated error. In Prieditis and Russell [6] Proceedings of the 11th International Conference on Machine Learning, Tahoe City, CA, 1995, Morgan Kaufmann, pages 304–312.Google Scholar
  5. 5.
    Donald Michie, David J. Spiegelhalter, and Charles C. Taylor, editors. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.Google Scholar
  6. 6.
    A. Prieditis and S. Russell, editors. Proceedings of the 11th International Conference on Machine Learning, Tahoe City, CA, 1995. Morgan Kaufmann.Google Scholar
  7. 7.
    L. Rendell, R. Seshu, and D. Tcheng. Layered-concept learning and dinamically variable bias management. In Proceedings of the 10th International Joint Conference on Artificial Intelligence, pages 308–314, Milan, Italy, 1987. Morgan Kaufmann.Google Scholar
  8. 8.
    G. Widmer. Recognition and exploitation of contextual cues via incremental metalearning. Technical Report OFAI-TR-96-01, Austria Research Institute for Artificial Intelligence, Vienna, Austria, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • José C. González
    • 1
  • Juan R. Velasco
    • 1
  • Carlos A. Iglesias
    • 1
  1. 1.Dept. Ingeniería de Sistemas TelemáticosUnivesidad Politécnica de MadridSpain

Personalised recommendations