A “Microscopic” Study of Minimum Entropy Search in Learning Decomposable Markov Networks

  • Authors:
  • Y. Xiang;S. K. M. Wong;N. Cercone

  • Affiliations:
  • Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada S4S 0A2 Email: yxiang, wong, nick@cs.uregina.ca;Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada S4S 0A2 Email: yxiang, wong, nick@cs.uregina.ca;Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada S4S 0A2 Email: yxiang, wong, nick@cs.uregina.ca

  • Venue:
  • Machine Learning
  • Year:
  • 1997

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Abstract

Several scoring metrics are used in different search procedures for learning probabilistic networks. We study the properties of cross entropy in learning a decomposable Markov network. Though entropy and related scoring metrics were widely used, its “microscopic” properties and asymptotic behavior in a search have not been analyzed. We present such a “microscopic” study of a minimum entropy search algorithm, and show that it learns an I-map of the domain model when the data size is large.Search procedures that modify a network structure one link at a time have been commonly used for efficiency. Our study indicates that a class of domain models cannot be learned by such procedures. This suggests that prior knowledge about the problem domain together with a multi-link search strategy would provide an effective way to uncover many domain models.