Django - theta-subsumption test for Relational Learning
Date of the last release: 01 June 2005
Person in charge :
SEBAG Michèle
Supervised learning intensively relies on the so-called covering test, checking whether a hypothesis covers an example. As the covering test is intensively used during the course of learning, its implementation must be efficient.
In Relational Learning and Inductive Logic Programming, the most commonly used test is the theta-subsumption defined by Plotkin. Based on reformulating theta-subsumption as a binary constraint satisfaction problem (CSP), the Django algorithm combines well-known CSP procedures and theta-subsumption specific data structures. The computational gain is about two orders of magnitude on the previous theta-subsumption algorithms.
Django has been devised by Jérôme Maloberti during his PhD under Michele Sebag's supervision. Why this name ? Because it's fast ! and because Jérôme is a Django Reinhardt's fan :-)
More information: http://tao.lri.fr/tiki-index.php?page=Django
Software - Licence :
GPL
Research activities
Machine learning
Members
SEBAG Michèle
Group
Learning and Optimization
Joint Inria project team TAO
