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Learning Probabilistic Relational Models

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Abstraction, Reformulation, and Approximation (SARA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1864))

Abstract

My work is on learning Probabilistic Relational Models (PRMs) from structured data (e.g., data in a relational database, an object-oriented database or a frame-based system). This work has as a starting point the framework of Probabilistic Relational Models, introduced in [5, 7]. We adapt and extend the machinery that has been developed over the years for learning Bayesian networks from data [1, 4, 6] to the task of learning PRMs from structured data. At the heart of this work is a search algorithm that explores the space of legal models using search operators that abstract or refine the model.

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References

  1. G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347, 1992.

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  2. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proc. IJCAI, 1999.

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  3. L. Getoor, D. Koller, B. Taskar, and N. Friedman. Discovering probablisitic models of relational structure. 2000. unpublished.

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  4. D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan, editor, Learning in Graphical Models. MIT Press, Cambridge, MA, 1998.

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  5. D. Koller and A. Pfeffer. Probabilistic frame-based systems. In Proc. AAAI, 1998.

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  6. W. Lam and F. Bacchus. Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence, 10:269–293, 1994.

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  7. A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University, 2000.

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Author information

Authors and Affiliations

  1. Computer Science Department, Stanford University, USA

    Lise Getoor

Authors
  1. Lise Getoor
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nebraska - Lincoln, 115 Ferguson Hall, Lincoln, NE, 68588-0115

    Berthe Y. Choueiry

  2. Department of Computer Science, University of York, Heslington, York, Y010 5DD, UK

    Toby Walsh

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© 2000 Springer-Verlag Berlin Heidelberg

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Getoor, L. (2000). Learning Probabilistic Relational Models. In: Choueiry, B.Y., Walsh, T. (eds) Abstraction, Reformulation, and Approximation. SARA 2000. Lecture Notes in Computer Science(), vol 1864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44914-0_25

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  • DOI: https://doi.org/10.1007/3-540-44914-0_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67839-7

  • Online ISBN: 978-3-540-44914-0

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