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ER
2004
Springer

Modeling Default Induction with Conceptual Structures

14 years 6 months ago
Modeling Default Induction with Conceptual Structures
Our goal is to model the way people induce knowledge from rare and sparse data. This paper describes a theoretical framework for inducing knowledge from these incomplete data described with conceptual graphs. The induction engine is based on a non-supervised algorithm named default clustering which uses the concept of stereotype and the new notion of default subsumption, the latter being inspired by the default logic theory. A validation using artificial data sets and an application concerning an historic case are given at the end of the paper.
Julien Velcin, Jean-Gabriel Ganascia
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ER
Authors Julien Velcin, Jean-Gabriel Ganascia
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