Results 71 to 80 of about 120 (110)

Inductive Synthesis of Logic Programs and Inductive Logic Programming

1994
Inductive Logic Programming deals with the problem of generating logic programs from examples, normally given as ground atoms. We briefly survey older methods (Shapiro’s MIS and Plotkin’s least general generalizations) which have set the foundations of the field and inspired more recent top-down and bottom-up approaches, respectively.
Francesco Bergadano, Daniele Gunetti
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Phonotactics in Inductive Logic Programming

2004
We examine the results of applying inductive logic programming (ILP) to a relatively simple linguistic task, that of recognizing monosyllables in one language. ILP is suited to linguistic problems given linguists' preference for formulating their theories in discrete rules, and because of ILP's ability to incorporate various background theories. But it
Nerbonne, J., Konstantopoulos, S.
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Inductive Logic Programming: Challenges

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
An overview of notable ILP areas, focusing on three invited talks at ILP 2015, two best student papers and the panel discussion on "ILP 25 Years".
Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto   +2 more
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Inductive logic programming and learnability

ACM SIGART Bulletin, 1994
The paper gives an overview of theoretical results in the rapidly growing field of inductive logic programming (ILP). The ILP learning situation (generality model, background knowledge, examples, hypotheses) is formally characterized and various restrictions of it are discussed in the light of their impact on learnability.
Jörg-Uwe Kietz, Saso Dzeroski
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April – An Inductive Logic Programming System [PDF]

Lecture Notes in Computer Science, 2006
Inductive Logic Programming (ILP) is a Machine Learning research field that has been quite successful in knowledge discovery in relational domains. ILP systems use a set of pre-classified examples (positive and negative) and prior knowledge to learn a theory in which positive examples succeed and the negative examples fail.
Fernando Silva, Rui Camacho, Nuno A Fonseca   +2 more
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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
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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