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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Research works printed but not linked: 13
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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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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