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Coper: A Methodology for Learning Invariant Functional Descriptions

  • Mieczyslaw M. Kokar
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 12)

Abstract

Functional descriptions constitute a significant class of goals for machine learning systems. Physical laws are one example of this kind of description. In this paper the COPER methodology is described, which allows discovery of functional descriptions from incomplete observational data. COPER eliminates irrelevant arguments, generates additional relevant argument descriptors (if some are missing), and generates a functional formula. The important feature of this methodology is that it allows testing of relevance for some of the attribute descriptors without varying their values throughout the training events. To do this, it utilizes the property of invariance of meaningful functional descriptions. One of the examples of COPER’s rediscoveries is Bernoulli’s law.

Keywords

Training Event Functional Description Attribute Descriptor Reasoning Engine Additional Descriptor 
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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Copyright information

© Kluwer Academic Publishers 1986

Authors and Affiliations

  • Mieczyslaw M. Kokar
    • 1
  1. 1.Northeastern UniversityDepartment of Industrial Engineering and Information SystemsBostonUSA

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