The DOI matches its Crossref record, with a small difference in title or year; 33 of the 38 references it cites were found in a registry or an index.
Induction of decision trees
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“For instance, it is impossible to provide a decision tree learner (Quinlan, 1986, 1993) this infinite relation because it would require an infinite feature table.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1986
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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, 1986
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“For instance, TILDE extends a decision tree learner (Quinlan, 1986, 1993) to the first-order setting and uses the same information gain methods to induce hypotheses.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1986
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“Fault tolerance and resilience are crucial in real-time monitoring to promptly detect failures and recover to minimize data loss and downtime [7].”
From Real-Time Monitoring of Data Pipelines: Exploring and Experimentally Proving that the Continuous Monitoring in Data Pipelines Reduces Cost and Elevates Quality (2024) · cites this paper as [7]
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Citations
Cites
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Flagged first, then by citations
5 of the 38 references couldn’t be matched to a known paper.
Research works printed but not linked: 13
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- Quinlan, J.R. (1983b). Learning from noisy data, Proceedings of the Second International Machine Learning Workshop. University of Illinois at Urbana-Champaign.
- Quinlan, J.R. (1985a). The effect of noise on concept learning. In R.S. Michalski, J.G. Carbonell & T.M. Mitchell (Eds.), Machine learning. Los Altos: Morgan Kaufmann (in press).
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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
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Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Metadata
- Authors
- Quinlan, J. R.
- Corresponding author
- Quinlan, J. R.
- Venue
- Machine Learning
- Published
- 1986-03
- Volume
- 1
- Issue
- 1
- Pages
- 81-106
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2149706766
- Cited by
- 21,439
- References
- 38
- Citations by year
- 2026: 899 · 2025: 1399 · 2024: 1161 · 2023: 1085 · 2022: 969 · 2021: 1165 · 2020: 1141 · 2019: 1064 · 2018: 877 · 2017: 801 · 2016: 700 · 2015: 633 · 2014: 687 · 2013: 680 · 2012: 681
- FWCI
- 255.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- decision tree induction, ID3, expert systems, noisy data, missing data, feature selection, classification
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