The Role of Occam's Razor in Knowledge Discovery
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The most common interpretation of an Occamist bias is that amongst all hypotheses consistent with the data, the simplest is the most likely.
“The most common interpretation of an Occamist bias is that amongst all hypotheses consistent with the data, the simplest is the most likely 28 .”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Domingos (1999)
Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287 · see citing works
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951 · see citing works
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · see citing works
Bernardo, J. M., & Smith, A. F. M. (1994) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316870 · Cited by 2,178 · see citing works
Schaffer, C. (1993) · Machine Learning · doi:10.1023/a:1022653209073 · Cited by 310 · see citing works
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · see citing works
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326 · see citing works
Self-citationDeveloping clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works
Prediction and explanation in social systems
Hofman, J. M., Sharma, A., & Watts, D. J. (2017) · Science · doi:10.1126/science.aal3856 · Cited by 429 · see citing works
Construct redundancy in leader behaviors: A review and agenda for the future
Banks, G. C., Gooty, J., Ross, R. L., Williams, C. E., & Harrington, N. T. (2018) · The Leadership Quarterly · doi:10.1016/j.leaqua.2017.12.005 · Cited by 275 · see citing works
Open issues in genetic programming
O’Neill, M., Vanneschi, L., Gustafson, S., & Banzhaf, W. (2010) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-010-9113-2 · Cited by 224 · see citing works
Inductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
“Domingos (1999) points out that the first razor is largely uncontroversial, but the second one, taken literally, is provably and empirically false (Zahálka & Zelezný, 2011).”
BackgroundInfluentialData-driven reverse engineering of signaling pathways using ensembles of dynamic models
Henriques, D., Villaverde, A. F., Rocha, M., Saez-Rodriguez, J., & Banga, J. R. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005379 · Cited by 44 · see citing works
PlÖtz, T. (2022) · ACM Computing Surveys · doi:10.1145/3459666 · Cited by 34 · see citing works
A survey on neuro-mimetic deep learning via predictive coding
Salvatori, T., Mali, A., Buckley, C. L., Lukasiewicz, T., Rao, R. P. N., Friston, K., & Ororbia, A. (2026) · Neural Networks · doi:10.1016/j.neunet.2025.108161 · Cited by 2 · see citing works
- Authors
- Domingos, P.
- Affiliations
- University of Washington (US)
- Corresponding author
- Domingos, Pedro
- ORCID
- Domingos, Pedro
- Venue
- Data Mining and Knowledge Discovery
- Published
- 1999-12
- Volume
- 3
- Issue
- 4
- Pages
- 409-425
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W1761022139
- Cited by
- 459
- References
- 5
- Citations by year
- 2026: 26 · 2025: 35 · 2024: 29 · 2023: 15 · 2022: 20 · 2021: 22 · 2020: 21 · 2019: 22 · 2018: 19 · 2017: 21 · 2016: 11 · 2015: 11 · 2014: 10 · 2013: 10 · 2012: 21
- FWCI
- 22.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Data Mining and Knowledge Discovery (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- Occam's razor, knowledge discovery, model simplicity, overfitting, domain constraints, model selection, model comprehensibility
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