UCI-ml
UC Irvine Machine Learning Repository. We currently maintain 251 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. Our old web site is still available, for those who prefer the old format. For a general overview of the Repository, please visit our About page. For information about citing data sets in publications, please read our citation policy. If you wish to donate a data set, please consult our donation policy. For any other questions, feel free to contact the Repository librarians. We have also set up a mirror site for the Repository. The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited ”papers” in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the donors and creators of the databases and data generators. Special thanks should also go to the past librarians of the repository: David Aha, Patrick Murphy, Christopher Merz, Eamonn Keogh, Cathy Blake, Seth Hettich, and David Newman.
Keywords for this software
References in zbMATH (referenced in 3100 articles )
Showing results 1 to 20 of 3100.
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- Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
- Gan, Guojun; Ma, Chaoqun; Wu, Jianhong: Data clustering. Theory, algorithms, and applications (2021)
- Ibrahim, Abdelmonem M.; Tawhid, Mohamed A.: A new hybrid binary algorithm of bat algorithm and differential evolution for feature selection and classification (2021)
- Ramsay, Kelly; Durocher, Stephane; Leblanc, Alexandre: Robustness and asymptotics of the projection median (2021)
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- Romano, Rosaria; Palumbo, Francesco: Partial possibilistic regression path modeling: handling uncertainty in path modeling (2021)
- Shih, Jia-Han; Emura, Takeshi: On the copula correlation ratio and its generalization (2021)
- Adona, V. A.; Gonçalves, M. L. N.; Melo, J. G.: An inexact proximal generalized alternating direction method of multipliers (2020)
- Afridi, Mohammad Khan; Azam, Nouman; Yao, JingTao: Variance based three-way clustering approaches for handling overlapping clustering (2020)
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- Alexandrov, Alexander; Benidis, Konstantinos; Bohlke-Schneider, Michael; Flunkert, Valentin; Gasthaus, Jan; Januschowski, Tim; Maddix, Danielle C.; Rangapuram, Syama; Salinas, David; Schulz, Jasper; Stella, Lorenzo; Türkmen, Ali Caner; Wang, Yuyang: GluonTS: probabilistic and neural time series modeling in Python (2020)
- Almodóvar-Rivera, Israel A.; Maitra, Ranjan: Kernel-estimated nonparametric overlap-based syncytial clustering (2020)
- Andrew G. Allmon, J.S. Marron, Michael G. Hudgens: diproperm: An R Package for the DiProPerm Test (2020) arXiv
- Arenz, Oleg; Zhong, Mingjun; Neumann, Gerhard: Trust-region variational inference with Gaussian mixture models (2020)
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- Babos, Stephen; Artemiou, Andreas: Sliced inverse median difference regression (2020)
- Badrinath, N.; Gopinath, G.; Ravichandran, K. S.; Premaladha, J.; Krishankumar, R.: Classification and prediction of erythemato-squamous diseases through tensor-based learning (2020)