PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Canonical Horn Representations and Query Learning
Marta Arias and José Balcázar
In: Algorithmic Learning Theory 2009, 3-5 Oct 2009, Porto, Portugal.


We describe an alternative construction of an existing canonical representation for definite Horn theories, the Guigues-Duquenne basis (or GD basis), which minimizes a natural notion of implicational size. We extend the canonical representation to general Horn, by providing a reduction from definite to general Horn CNF. We show how this representation relates to two topics in query learning theory: first, we show that a well-known algorithm by Angluin, Frazier and Pitt that learns Horn CNF always outputs the GD basis independently of the counterexamples it receives; second, we build strong polynomial certificates for Horn CNF directly from the GD basis.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5817
Deposited By:Marta Arias
Deposited On:08 March 2010