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 3127 articles )

Showing results 1 to 20 of 3127.
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  1. Ai, Mingyao; Wang, Fei; Yu, Jun; Zhang, Huiming: Optimal subsampling for large-scale quantile regression (2021)
  2. Allassonnière, Stéphanie; Chevallier, Juliette: A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling (2021)
  3. Bagirov, Adil M.; Taheri, Sona; Cimen, Emre: Incremental DC optimization algorithm for large-scale clusterwise linear regression (2021)
  4. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  5. Bertsimas, Dimitris; Dunn, Jack; Wang, Yuchen: Near-optimal nonlinear regression trees (2021)
  6. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  7. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  8. Gan, Guojun; Ma, Chaoqun; Wu, Jianhong: Data clustering. Theory, algorithms, and applications (2021)
  9. Hu, Shengwei; Wang, Yong: Modal clustering using semiparametric mixtures and mode flattening (2021)
  10. Ibrahim, Abdelmonem M.; Tawhid, Mohamed A.: A new hybrid binary algorithm of bat algorithm and differential evolution for feature selection and classification (2021)
  11. Prakaash, A. S.; Sivakumar, K.: Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: scope towards diverse applications (2021)
  12. Ramsay, Kelly; Durocher, Stephane; Leblanc, Alexandre: Robustness and asymptotics of the projection median (2021)
  13. Rebrova, Elizaveta; Needell, Deanna: On block Gaussian sketching for the Kaczmarz method (2021)
  14. Romano, Rosaria; Palumbo, Francesco: Partial possibilistic regression path modeling: handling uncertainty in path modeling (2021)
  15. Shih, Jia-Han; Emura, Takeshi: On the copula correlation ratio and its generalization (2021)
  16. Watanabe, Chihiro; Suzuki, Taiji: Goodness-of-fit test for latent block models (2021)
  17. Zeng, Yaohui; Yang, Tianbao; Breheny, Patrick: Hybrid safe-strong rules for efficient optimization in Lasso-type problems (2021)
  18. Adona, V. A.; Gonçalves, M. L. N.; Melo, J. G.: An inexact proximal generalized alternating direction method of multipliers (2020)
  19. Afridi, Mohammad Khan; Azam, Nouman; Yao, JingTao: Variance based three-way clustering approaches for handling overlapping clustering (2020)
  20. Akhanli, Serhat Emre; Hennig, Christian: Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes (2020)

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