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The Role of Occam's Razor in Knowledge Discovery

Domingos, Pedro·1999·Data Mining and Knowledge Discovery
DOI 10.1023/a:1009868929893
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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)

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Metadata

Authors
Domingos, P.
Affiliations
University of Washington (US)
Corresponding author
Domingos, Pedro
ORCID
Domingos, Pedro
Venue
Data Mining and Knowledge Discovery
Publisher
Springer Science and Business Media LLC
Published
1999-12
Volume
3
Issue
4
Pages
409-425
ISSN
  • 1384-5810 (ISSN-L)
  • 1573-756X
DOI
10.1023/a:1009868929893
OpenAlex
W1761022139
Cited by
459
References
6
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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