Kietz/Morik/94a: A Polynomial Approach to the Constructive Induction of Structural Knowledge

Bibtype Article
Bibkey Kietz/Morik/94a
Author Kietz, Jörg-Uwe and Morik,Katharina
Ls8autor Morik, Katharina
Title A Polynomial Approach to the Constructive Induction of Structural Knowledge
Journal Machine Learning Journal
Volume 14
Number 2
Pages 193 -- 217
Institution GMD (German Natl. Research Center for Computer Science)
Address P.O.Box 1240, W-5205 St. Augustin 1, Germany
Abstract The representation formalism as well as the representation language is of great importance for the success of machine learning. The representation formalism should be expressive, efficient, useful, and applicable. First-order logic needs to be restricted in order to be efficient for inductive and deductive reasoning. In the field of knowledge representation, term subsumption formalisms have been developed which are efficient and expressive. In this article, a learning algorithm, KLUSTER, is described that represents concept definitions in this formalism. KLUSTER enhances the representation language if this is necessary for the discrimination of concepts. Hence, KLUSTER is a constructive induction program. KLUSTER builds the most specific generalization and a most general discrimination in polynomial time. It embeds these concept learning problems into the overall task of learning a hierarchy of concepts.
Month feb
Year 1994
Projekt ILP
Url http://springerlink.metapress.com/app/home/contribution.asp?wasp=c7f0ff7741944ec8abdcbf6b36479b0c&referrer=parent&backto=issue,4,6;journal,101,139;linkingpublicationresults,1:100309,1
Inductive Logic Programming