PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Construction and learnability of canonical Horn formulas
Marta Arias and José Balcázar
Machine Learning Volume 85, Number 3, pp. 273-297, 2011. ISSN 0885-6125

Abstract

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. Using these tools, we provide a new, simpler validation of the classic Horn query learning algorithm of Angluin, Frazier, and Pitt, and we prove that this algorithm always outputs the GD basis regardless of the counterexamples it receives.

??
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8023
Deposited By:Marta Arias
Deposited On:17 March 2011