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“Second, the field of data science has historically focused on certain tasks and evaluation metrics 2 that are not applicable to some of climate science's biggest needs.”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [2]
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“Traditional data science, and machine learning specifically, has relied on attribute-value data as input to most learning models.2”
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Metadata
- Authors
- Langley, P.
- Venue
- Machine Learning
- Published
- 2011-02-18
- Volume
- 82
- Issue
- 3
- Pages
- 275-279
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2038507439
- Cited by
- 174
- References
- 11
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 14 · 2023: 21 · 2022: 19 · 2021: 29 · 2020: 17 · 2019: 15 · 2018: 9 · 2017: 8 · 2016: 3 · 2015: 8 · 2014: 5 · 2013: 5 · 2012: 6
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- ResearchSpace (University of Auckland) (submittedVersion)
- Topics
- Social Sciences › Library and Information Sciences › Publishing and Scholarly Communication
- Physical Sciences › Computer Science › Computer Science Applications › History of Computing Technologies
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Historical Studies and Socio-cultural Analysis
- Keywords
- history of machine learning, journal founding, computational learning theory, journal editorial history
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