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A study of relevance for learning in deductive databases

✍ Scribed by Nada Lavrač; Dragan Gamberger; Viktor Jovanoski

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
288 KB
Volume
40
Category
Article
ISSN
0743-1066

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✦ Synopsis

This paper is a study of the problem of relevance in inductive concept learning. It gives definitions of irrelevant literals and irrelevant examples and presents ecient algorithms that enable their elimination. The proposed approach is directly applicable in propositional learning and in relation learning tasks that can be solved using a LINUS transformation approach. A simple inductive logic programming (ILP) problem is used to illustrate the approach to irrelevant literal and example elimination. Results of utility studies show the usefulness of literal reduction applied in LINUS and in the search of re®nement graphs.

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