Chapter

Inductive Logic Programming

Volume 2583 of the series Lecture Notes in Computer Science pp 198-206

Date:

Learning Structure and Parameters of Stochastic Logic Programs

  • Stephen MuggletonAffiliated withDepartment of Computing, Imperial College

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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