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Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis

  • Conference paper
Logic Programming (ICLP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5649))

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Abstract

A class of probabilistic-logic models is considered, which increases the expressibility from HMM’s and SCFG’s regular and context-free languages to, in principle, Turing complete languages. In general, such models are computationally far too complex for direct use, so optimization by pruning and approximation are needed. The first steps are taken towards a methodology for optimizing such models by approximations using auxiliary models for preprocessing or splitting them into submodels. Evaluation of such approximating models is challenging as authoritative test data may be sparse. On the other hand, the original complex models may be used for generating artificial evaluation data by efficient sampling, which can be used in the evaluation, although it does not constitute a foolproof test procedure. These models and evaluation processes are illustrated in the PRISM system developed by other authors, and we discuss their applicability and limitations.

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Author information

Authors and Affiliations

  1. Research group PLIS: Programming, Logic and Intelligent Systems Department of Communication, Business and Information Technologies, Roskilde University, P.O.Box 260, DK-4000, Roskilde, Denmark

    Henning Christiansen & Ole Torp Lassen

Authors
  1. Henning Christiansen
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  2. Ole Torp Lassen
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Editor information

Editors and Affiliations

  1. School of Computing, University of Leeds, LS2 9JT, Leeds, UK

    Patricia M. Hill

  2. Deptartment of Computer Science, Stony Brook University, 11794-4400, Stony Brook, NY, USA

    David S. Warren

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© 2009 Springer-Verlag Berlin Heidelberg

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Christiansen, H., Torp Lassen, O. (2009). Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis. In: Hill, P.M., Warren, D.S. (eds) Logic Programming. ICLP 2009. Lecture Notes in Computer Science, vol 5649. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02846-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-02846-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02845-8

  • Online ISBN: 978-3-642-02846-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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