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A declarative language bias for levelwise search of first-order
regularities
Irene Weber.
In Fritz Wysotzki,
Peter Geibel,
and Christina Schädler, editors,
Proc. Fachgruppentreffen Maschinelles Lernen (FGML-98), Technischer
Bericht 98/11 des Fachbereiches Informatik, pages 106--113. TU Berlin, August
1998.
Abstract
(longer version) The paper presents a hypothesis language declaration formalism
and a corresponding refinement operator that successfully combines the
levelwise search principle with a first-order hypothesis language and thus
provides an improvement to the so-called optimal refinement operators that
are commonly used in descriptive ILP. The refinement operator is based on the
candidate generation procedure of the Apriori algorithm. It extends the
Apriori candidate generation in that it allows to define constraints on the
combinations of literals in the hypotheses. Experimental results show the
usefulness of the approach.
BibTeX entry.
Other publications
I Weber,
Irene.Weber@informatik.uni-stuttgart.de. Last modified on Wednesday 9 April 2003 at 18:31. © 2003 ILPnet2