Learning, Inferences and Iterations

  • Michael M. Richter
Conference paper

Abstract

The concept of learning is studied in several disciplines such as Artificial Intelligence, Mathematics or Cognitive Sciences. All these approaches are somehow related to each other but they also show some fundamental differences. The lack of a uniform terminology makes a comparison difficult. We will give here some prototypical examples of different methods, discuss some of their relations and make some first steps towards an integrated view.

Keywords

Boolean Function Logical Predicate Analogical Reasoning Integrate View Geometric Investigation 
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.

References

  1. M. Stadler: Ein Vergleich von fallbasierten induktive und statistischen Lernverfahren für die Klassifikation. Diplomarbeit Kaiserslautern 1991.Google Scholar
  2. M.M. Richter, S. Weß: Similarity, Uncertainty and Case-Based Reasoning in PATDEX. In: Frontiers of Computing, Ed. R.S. Boyer.Google Scholar
  3. X. Li: On the entropy potential of binary functions. Preprint Kaiserslautern, 1991.Google Scholar

Copyright information

© Physica-Verlag Heidelberg 1992

Authors and Affiliations

  • Michael M. Richter
    • 1
  1. 1.KaiserslauternGermany

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