The DOI matches its Crossref record; 40 of the 45 references it cites were found in a registry or an index.
Learning Logical Definitions from Relations
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“ILP systems such as ALEPH (Srinivasan 2001) and FOIL (Quinlan 1990) have no predicate invention and limited recursion learning and therefore cannot learn recursive grammars from example sequences.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Quinlan 1990
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Indeed, many popular ILP systems, such as FOIL (Quinlan 1990), Progol (Muggleton 1995), TILDE (Blockeel and De Raedt 1998), and Aleph (Srinivasan 2001) struggle to learn recursive programs.”
From Learning programs by learning from failures (2021) · cites this paper as Quinlan 1990
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Although early work in ILP explored this form of learning (Sammut, 1981; Quinlan, 1990), it has been under-explored until recently (Lin et al., 2014; Cropper, 2019, 2020; Hocquette & Muggleton, 2020; Dumancic et al., 2021), mostly because of new techniques for PI.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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“Quinlan's (1990) FOIL system is one of the most well-known ILP systems and is a natural extension of ID3 (Quinlan, 1986) from the propositional setting to the first-order setting.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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“The above paragraph refers only to generality orders on single clauses, as many systems employ the covering algorithm whereby hypotheses are constructed iteratively clause by clause (Quinlan, 1990; De Raedt & Dehaspe, 1997; Muggleton, 1995; Blockeel & De Raedt, 1998; Srinivasan, 2001).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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“Top-down algorithms, (Quinlan, 1990; Blockeel & De Raedt, 1998; Bratko, 1999; Muggleton et al., 2008) start with a general hypothesis and then specialise it.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Self-citationClassification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553
Self-citationClark, P. A., & Niblett, T. (1989) · Machine Learning · doi:10.1023/a:1022641700528 · Cited by 2,267
A Universal Prior for Integers and Estimation by Minimum Description Length
Rissanen, J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346150 · Cited by 1,655
Explanation-Based Generalization: A Unifying View
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986) · Machine Learning · doi:10.1023/a:1022691120807 · Cited by 1,382
Expert systems in the micro-electronic age
Michie, D. (1979) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,119
Shapiro, E. Y. (1983) · doi:10.7551/mitpress/1192.001.0001 · Cited by 1,085
Research works printed but not linked: 2
- Brebner, P.C. (1988). Gargantubrain: An heuristic algorithm for learning non-recursive Horn clauses from positive and negative examples. (Technical Report 8802). Sydney, Australia: University of New South Wales, School of Electrical Engineering and Computer Science.
- Schlimmer, J.C., and Fisher, D. (1986). A case study of incremental concept formation. Proceedings of the Fifth National Conference on Artificial Intelligence (pp.296-501). Philadelphia, PA: Morgan Kaufmann.
Cited by
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Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Cohen, W. W. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,890
A Review of Relational Machine Learning for Knowledge Graphs
Nickel, M., Murphy, K. P., Volker Tresp, C., & Gabrilovich, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,780
Inductive Logic Programming: Theory and methods
Muggleton, S., & de Raedt, L. (1994) · The Journal of Logic Programming · doi:10.1016/0743-1066(94)90035-3 · Cited by 1,537
Statistical Fraud Detection: A Review
Bolton, R. J., & Hand, D. J. (2002) · Statistical Science · doi:10.1214/ss/1042727940 · Cited by 1,536
Metadata
- Authors
- Quinlan, J. R.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Quinlan, J.R.
- Venue
- Machine Learning
- Published
- 1990-08
- Volume
- 5
- Issue
- 3
- Pages
- 239-266
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1999138184
- Cited by
- 1,644
- References
- 45
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 21 · 2023: 23 · 2022: 22 · 2021: 34 · 2020: 34 · 2019: 26 · 2018: 36 · 2017: 29 · 2016: 25 · 2015: 29 · 2014: 44 · 2013: 44 · 2012: 41
- FWCI
- 47.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Australian Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- foil, inductive logic programming, Horn clauses, relational data, first-order logic, rule induction
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