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ICLP
2009
Springer

Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis

12 years 5 months ago
Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis
Abstract. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and contextfree 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.
Henning Christiansen, Ole Torp Lassen
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2009
Where ICLP
Authors Henning Christiansen, Ole Torp Lassen
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