Inductive logic programming (Book, 1992) []
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Inductive logic programming

Author: Stephen Muggleton
Publisher: London : Academic, ©1992.
Series: A.P.I.C. studies in data processing, no. 38.
Edition/Format:   Print book : EnglishView all editions and formats

Whilst inheriting various positive characteristics of the parent subjects of logic programming and machine learning, it is hoped that inductive logic programming will overcome many of the limitations  Read more...


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Additional Physical Format: Online version:
Inductive logic programming.
London : Academic, ©1992
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Stephen Muggleton
ISBN: 0125097158 9780125097154
OCLC Number: 27108041
Description: xiv, 565 pages : illustrations ; 24 cm
Contents: Inductive logic programming, S. Muggleton; extensions of inversion of resolution applied to theory completion, Celine Rouveirol; generalization and learnability - a study of constrained atoms, C.D. Page Jr and A.M. Frisch; learning theoretical terms, R.B. Banerji; logic programme synthesis from good examples, C.X. Ling; a critical comparison of various methods based on inverse resolution, C.X. Ling and M.A. Narayan; non-monotonic learning, M. Bain and S. Muggleton; an overview of the interactive concept-learner and theory revisor, Clint; a framework for inductive logic programming, P.A. Flach; the rule-based systems project - using confirmation theory and non-monotonic logics for incremental learning, D. Gabbay, et al; relating relational learning algorithms, D.W. Aha; machine intervention of first-order predicates by inverting resolution, S. Muggleton and W. Buntine; efficient induction of logic programmes, S. Muggleton and C. Feng; constraints for predicate invention, R. Wirth and P. O'Rorke; refinement graphs for FOIL and LINUS, S. Czeroski and N. Lavrac; controlling the complexity of learning in logic through syntactic and task-oriented models, J.U. Kietz and S. Wrobel; efficient learning of logic programme with non-determinate, non-discriminating literals, B. Kijsirikul, et al; an information-based approach to integrating empirical and explanation-based learning, M.J. Pazzani, et al; analogical reasoning for logic programming, B. Tausend and S. Bell; some thoughts on inverse resolution, G. Sablon, et al; experiments in non-monotonic first-order induction, M.Bain; learning qualitative models of dynamic systems, I. Bratko, et al; the application of inductive logic programming to finite element mesh design, B. Dolsak and S. Muggleton; inducing temporal fault diagnostic rules from a qualitative model, C. Feng; in ductive learning of relations from noisy examples, N. Lavrac and S. Dzeroski; learning chess patterns, E. Morales; applying inductive logic programming in reactive environments, D. Hume and C. Sammut.
Series Title: A.P.I.C. studies in data processing, no. 38.
Responsibility: edited by Stephen Muggleton.
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