List of datasets for machine-learning research

These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.[2][3][4][5]

Many organizations including governments publish and share their datasets. The datasets are classified, based on the licenses, as Open data and Non-Open data.

The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are made available as various sorted types and subtypes.

  1. ^ Wissner-Gross, A. "Datasets Over Algorithms". Edge.com. Retrieved 8 January 2016.
  2. ^ Weiss, G. M.; Provost, F. (1 September 2003). "Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction". Journal of Artificial Intelligence Research. 19. AI Access Foundation: 315–354. doi:10.1613/jair.1199. ISSN 1076-9757. S2CID 2344521.
  3. ^ Turney, Peter (2000). "Types of cost in inductive concept learning". arXiv:cs/0212034.
  4. ^ Abney, Steven (17 September 2007). Semisupervised Learning for Computational Linguistics. CRC Press. ISBN 978-1-4200-1080-0.
  5. ^ Žliobaitė, Indrė; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoff (2011). "Active Learning with Evolving Streaming Data". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 6913. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 597–612. doi:10.1007/978-3-642-23808-6_39. ISBN 978-3-642-23807-9. ISSN 0302-9743.

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