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Some experimental results on learning probabilistic and possibilistic networks with different evaluation measures

  • Christian Borgelt
  • Rudolf Kruse
Invited Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1244)

Abstract

A large part of recent research on probabilistic and possibilistic inference networks has been devoted to learning them from data. In this paper we discuss two search methods and several evaluation measures usable for this task. We consider a scheme for evaluating induced networks and present experimental results obtained from an application of INES (Induction of NEtwork Structures), a prototype implementation of the described methods and measures.

Keywords

Mutual Information Evaluation Measure Marginal Distribution Domain Expert Information Gain 
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

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Christian Borgelt
    • 1
  • Rudolf Kruse
    • 1
  1. 1.Department of Information and Communication SystemsOtto-von-Guericke-University of MagdeburgMagdeburgGermany

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