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.


References in zbMATH (referenced in 2537 articles )

Showing results 1 to 20 of 2537.
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  1. Baumann, P.; Hochbaum, D. S.; Yang, Y. T.: A comparative study of the leading machine learning techniques and two new optimization algorithms (2019)
  2. Devarakonda, Aditya; Fountoulakis, Kimon; Demmel, James; Mahoney, Michael W.: Avoiding communication in primal and dual block coordinate descent methods (2019)
  3. Dubitzky, Werner; Lopes, Philippe; Davis, Jesse; Berrar, Daniel: The Open International Soccer Database for machine learning (2019)
  4. Gao, Can; Lai, Zhihui; Zhou, Jie; Wen, Jiajun; Wong, Wai Keung: Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction (2019)
  5. Hsu, Hsiang-Ling; Chang, Yuan-chin Ivan; Chen, Ray-Bing: Greedy active learning algorithm for logistic regression models (2019)
  6. Karaca, Yeliz; Cattani, Carlo: Computational methods for data analysis (2019)
  7. Liu, Guilong; Hua, Zheng: A general reduction method for fuzzy objective relation systems (2019)
  8. Li, Weiwei; Jia, Xiuyi; Wang, Lu; Zhou, Bing: Multi-objective attribute reduction in three-way decision-theoretic rough set model (2019)
  9. Li, Zibo; Sun, Guangmin; He, Cunfu; Liu, Xiucheng; Zhang, Ruihuan; Li, Yu; Zhao, Dequn; Liu, Hao; Zhang, Fan: Multi-variable regression methods using modified Chebyshev polynomials of class 2 (2019)
  10. Melnykov, Volodymyr; Zhu, Xuwen: An extension of the $K$-means algorithm to clustering skewed data (2019)
  11. Qian, Jin; Liu, Caihui; Yue, Xiaodong: Multigranulation sequential three-way decisions based on multiple thresholds (2019)
  12. Slawski, Martin; Ben-David, Emanuel: Linear regression with sparsely permuted data (2019)
  13. Uiwon Hwang, Dahuin Jung, Sungroh Yoon: HexaGAN: Generative Adversarial Nets for Real World Classification (2019) arXiv
  14. Wu, Yan-Xue; Min, Xue-Yang; Min, Fan; Wang, Min: Cost-sensitive active learning with a label uniform distribution model (2019)
  15. Zhao, Hong; Yu, Shenglong: Cost-sensitive feature selection via the $\ell_2,1$-norm (2019)
  16. Aduenko, Alexander A.; Motrenko, Anastasia P.; Strijov, Vadim V.: Object selection in credit scoring using covariance matrix of parameters estimations (2018)
  17. Afridi, Mohammad Khan; Azam, Nouman; Yao, JingTao; Alanazi, Eisa: A three-way clustering approach for handling missing data using GTRS (2018)
  18. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  19. Ah-Pine, Julien: An efficient and effective generic agglomerative hierarchical clustering approach (2018)
  20. Aloise, Daniel; Contardo, Claudio: A sampling-based exact algorithm for the solution of the minimax diameter clustering problem (2018)

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