The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
OpenML: networked science in machine learning
Open accessAbstract
Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.
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Metadata
- Authors
- Vanschoren, J., van Rijn, J. N., Bischl, B., & Torgo, L.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2014-06-16
- Volume
- 15
- Issue
- 2
- Pages
- 49-60
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2132862423
- Cited by
- 1,141
- References
- 7
- FWCI
- 85.98
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
