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Applications of inductive logic programming

Communications of the ACM, 1995
Techniques of machine learning have been successfully applied to various problems [1, 12]. Most of these applications rely on attribute-based learning, exemplified by the induction of decision trees as in the program C4.5 [20]. Broadly speaking, attribute-based learning also includes such approaches to learning as neural networks and nearest neighbor ...
Ivan Bratko, Stephen H. Muggleton
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Inductive logic programming

New Generation Computing, 1991
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Bayesian Inductive Logic Programming

Proceedings of the seventh annual conference on Computational learning theory - COLT '94, 1994
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learning and Computational Learning Theory, ILP is based on lock-step development of Theory, Implementations and Applications.
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Applications of inductive logic programming

ACM SIGART Bulletin, 1994
Some applications of Inductive Logic Programming (ILP) are presented. Those applications are chosen that specifically benefit from relational descriptions generated by ILP programs, and from ILP's ability to accommodate background knowledge.
Bratko, Ivan, King, Ross D
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Cautious induction in inductive logic programming

1997
Many top-down Inductive Logic Programming systems use a greedy, covering approach to construct hypotheses. This paper presents an alternative, cautious approach, known as cautious induction. We conjecture that cautious induction can allow better hypotheses to be found, with respect to some hypothesis quality criteria.
Simon Anthony, Alan M. Frisch
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Possibilistic Inductive Logic Programming

2005
Learning rules with exceptions may be of interest, especially if the exceptions are not important in some sense. Standard Inductive Logic Programming (ILP) algorithms and classical first order logic are not well-suited for managing rules with exceptions.
Mathieu Serrurier, Henri Prade
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Inductive Equivalence of Logic Programs

2005
This paper studies equivalence issues in inductive logic programming. A background theory B1 is inductively equivalent to another background theory B2 if B1 and B2 induce the same hypotheses for any given set of examples. Inductive equivalence is useful to compare inductive capabilities among agents having different background theories.
Chiaki Sakama, Katsumi Inoue
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Induction of Constraint Logic Programs

1996
Inductive Logic Programming is mainly concerned with the problem of learning concept definitions from positive and negative examples of these concepts and background knowledge. Because of complexity problems, the underlying first order language is often restricted to variables, predicates and constants.
Lionel Martin, Christel Vrain
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Inductive logic programming beyond logical implication

1996
This paper discusses the generalization of definite Horn programs beyond the ordering of logical implication. Since the seminal paper on generalization of clauses based on θ subsumption, there are various extensions in this area. Especially in inductive logic programming(ILP), people are using various methods that approximate logical implication, such ...
Jianguo Lu, Jun Arima
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Probabilistic Inductive Logic Programming on the Web

2017
Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning
RIGUZZI, Fabrizio, ZESE, Riccardo, COTA, Giuseppe   +2 more
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