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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In the citing paper
“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.
In the citing paper
“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.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
Flagged first, then by citations
5 of the 45 references couldn’t be matched to a known paper.
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.
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Huang, Y., Volker Tresp, C., Nickel, M., Rettinger, A., & Kriegel, H. (2014) · Semantic Web · doi:10.3233/sw-130100 · Cited by 30
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Pac-learning non-recursive prolog clauses
Cohen, W. W. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(94)00034-4 · Cited by 28
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Bissantz, N., & Hagedorn, J. (2008) · WIRTSCHAFTSINFORMATIK · doi:10.1007/s11576-008-0108-z · Cited by 28
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Sun, R., Peterson, T. S., & Merrill, E. C. (1999) · Applied Intelligence · doi:10.1023/a:1008332731824 · Cited by 28
Learning to set up numerical optimizations of engineering designs
Schwabacher, M., Ellman, T., & Hirsh, H. (1998) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060498122084 · Cited by 28
Inductive learning in deductive databases
Džeroski, S., & Nada Lavrač, A. (1993) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.250076 · Cited by 28
TDIS : an Algebraic Formalization.
Ganascia, J. (1993) · Cited by 28
Evaluating a Simulated Student using Real Students Data for Training and Testing
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Das, R., Godbole, A., Monath, N., Zaheer, M., & McCallum, A. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.427 · Cited by 27
Z-number-valued rule-based decision trees
Li, Y., Herrera‐Viedma, E., Kou, G., & Morente-Molinera, J. A. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119252 · Cited by 27
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An Analysis of the FURIA Algorithm for Fuzzy Rule Induction
Hühn, J., & Hüllermeier, E. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05177-7_16 · Cited by 27
Learning ontology rules for semantic video annotation
Bertini, M., Bimbo, A. D., & Serra, G. (2008) · doi:10.1145/1460676.1460678 · Cited by 27
How Does Predicate Invention Affect Human Comprehensibility?
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Ghalwash, A. Z. (1998) · Applied Intelligence · doi:10.1023/a:1008311702940 · Cited by 27
Pac-learning a restricted class of recursive logic programs
Cohen, W. W. (1993) · Cited by 27
Comparison of graph-based and logic-based multi-relational data mining
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O’Connell, M.-L., Howley, T., Ryder, A. G., Léger, M. N., & Madden, M. G. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.605156 · Cited by 24
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Quinlan, J. R. (1991) · IEEE Expert · doi:10.1109/64.108949 · Cited by 24
Self-citationKrishnaswamy, N., Friedman, S., & Pustejovsky, J. D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33012911 · Cited by 2
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Fürnkranz, J., Petrak, J., & Trappl, R. (1997) · Applied Artificial Intelligence · doi:10.1080/088395197118262 · Cited by 23
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Pérez, E., & Rendell, L. A. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50062-1 · Cited by 23
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Giordana, A., Neri, F., Saitta, L., & Botta, M. (1997) · Machine Learning · doi:10.1023/a:1007361708126 · Cited by 23
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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