Learning Structure and Parameters of Stochastic Logic Programs

  • Stephen Muggleton
Conference paper

DOI: 10.1007/3-540-36468-4_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2583)
Cite this paper as:
Muggleton S. (2003) Learning Structure and Parameters of Stochastic Logic Programs. In: Matwin S., Sammut C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science, vol 2583. Springer, Berlin, Heidelberg

Abstract

Previous papers have studied learning of Stochastic Logic Programs (SLPs) either as a purely parametric estimation problem or separated structure learning and parameter estimation into separate phases. In this paper we consider ways in which both the structure and the parameters of an SLP can be learned simultaneously. The paper assumes an ILP algorithm, such as Progol or FOIL, in which clauses are constructed independently. We derive analytical and numerical methods for efficient computation of the optimal probability parameters for a single clause choice within such a search.

Keywords

Stochastic logic programs generalisation analytical methods numerical methods 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Stephen Muggleton
    • 1
  1. 1.Department of ComputingImperial CollegeLondonUK

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