Inductive Learning in Deductive Databases.
Saso Dzeroski, Nada Lavrac:
Inductive Learning in Deductive Databases.
IEEE Trans. Knowl. Data Eng. 5(6): 939-949(1993)@article{DBLP:journals/tkde/DzeroskiL93,
author = {Saso Dzeroski and
Nada Lavrac},
title = {Inductive Learning in Deductive Databases},
journal = {IEEE Trans. Knowl. Data Eng.},
volume = {5},
number = {6},
year = {1993},
pages = {939-949},
ee = {db/journals/tkde/DzeroskiL93.html},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
Most current applications of inductive learning in
databases take place in the context of a single extensional
relation. The authors place inductive learning in the
context of a set of relations defined either extensionally
or intentionally in the framework of deductive databases.
LINUS, an inductive logic programming system that induces
virtual relations from example positive and negative tuples
and already defined relations in a deductive database, is
presented. Based on the idea of transforming the problem of
learning relations to attribute-value form, several
attribute-value learning systems are incorporated. As the
latter handle noisy data successfully, LINUS is able to
learn relations from real-life noisy databases. The use of
LINUS for learning virtual relations is illustrated, and a
study of its performance on noisy data is presented.
Copyright © 1993 by The Institute of
Electrical and Electronic Engineers, Inc. (IEEE).
Abstract used with permission.
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BibTeX
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Contents BibTeX
Referenced by
- Beat Wüthrich:
Probabilistic Knowledge Bases.
IEEE Trans. Knowl. Data Eng. 7(5): 691-698(1995)
- Christopher J. Matheus, Philip K. Chan, Gregory Piatetsky-Shapiro:
Systems for Knowledge Discovery in Databases.
IEEE Trans. Knowl. Data Eng. 5(6): 903-913(1993)
BibTeX
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