2007 AnILPRefinementOperatorforBiolo

• (Fredouille et al., 2007) ⇒ Daniel C. Fredouille, Christopher H. Bryant, Channa K. Jayawickreme, Steven Jupe, and Simon Topp. (2007). “An ILP Refinement Operator for Biological Grammar Learning.” In: Inductive Logic Programming. ISBN:978-3-540-73846-6 doi:10.1007/978-3-540-73847-3_24

Subject Headings: Refinement Operator, ILP Refinement Operator.

Cited By

• http://scholar.google.com/scholar?q=%222007%22+An+ILP+Refinement+Operator+for+Biological+Grammar+Learning
• http://dl.acm.org/citation.cfm?id=1417644.1417671&preflayout=flat#citedby

Quotes

Abstract

We are interested in using Inductive Logic Programming (ILP) to infer grammars representing sets of biological sequences. We call these biological grammars. ILP systems are well suited to this task in the sense that biological grammars have been represented as logic programs using the Definite Clause Grammar or the String Variable Grammar formalisms. However, the speed at which ILP systems can generate biological grammars has been shown to be a bottleneck. This paper presents a novel refinement operator implementation, specialised to infer biological grammars with ILP techniques. This implementation is shown to significantly speed-up inference times compared to the use of the classical refinement operator: time gains larger than 5-fold were observed in 45 of the experiments, and the maximum observed gain is over 300-fold.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 AnILPRefinementOperatorforBiolo	Daniel C. Fredouille Christopher H. Bryant Channa K. Jayawickreme Steven Jupe Simon Topp			An ILP Refinement Operator for Biological Grammar Learning				10.1007/978-3-540-73847-3_24		2007