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 2011 articles )

Showing results 1 to 20 of 2011.
Sorted by year (citations)

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  1. Abaszade, Maryam; Effati, Sohrab: Support vector regression with random output variable and probabilistic constraints (2017)
  2. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  3. Benjamin R. Fitzpatrick, Kerrie Mengersen: A network flow approach to visualising the roles of covariates in random forests (2017) arXiv
  4. Borgelt, Christian; Kruse, Rudolf: Agglomerative fuzzy clustering (2017)
  5. Cancelliere, R.; Deluca, R.; Gai, M.; Gallinari, P.; Rubini, L.: An analysis of numerical issues in neural training by pseudoinversion (2017)
  6. Chou, Chun-An; Bonates, Tibérius O.; Lee, Chungmok; Chaovalitwongse, Wanpracha Art: Multi-pattern generation framework for logical analysis of data (2017)
  7. Elmoataz, Abderrahim; Lozes, François; Toutain, Matthieu: Nonlocal PDEs on graphs: from Tug-of-War games to unified interpolation on images and point clouds (2017)
  8. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  9. Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
  10. Ghiglietti, Andrea; Ieva, Francesca; Paganoni, Anna Maria; Aletti, Giacomo: On linear regression models in infinite dimensional spaces with scalar response (2017)
  11. Karasuyama, Masayuki; Mamitsuka, Hiroshi: Adaptive edge weighting for graph-based learning algorithms (2017)
  12. Kiran, B.Ravi; Serra, Jean: Cost-complexity pruning of random forests (2017)
  13. Marjolein Fokkema: pre: An R Package for Fitting Prediction Rule Ensembles (2017) arXiv
  14. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  15. Razzaghi, Talayeh; Xanthopoulos, Petros; Şeref, Onur: Constraint relaxation, cost-sensitive learning and bagging for imbalanced classification problems with outliers (2017)
  16. Sasikala, S.; Appavu alias Balamurugan, S.; Geetha, S.: A novel adaptive feature selector for supervised classification (2017)
  17. Schmidt, Mark; Le Roux, Nicolas; Bach, Francis: Minimizing finite sums with the stochastic average gradient (2017)
  18. Şeref, Onur; Razzaghi, Talayeh; Xanthopoulos, Petros: Weighted relaxed support vector machines (2017)
  19. Ting, Kai Ming; Washio, Takashi; Wells, Jonathan R.; Aryal, Sunil: Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors (2017)
  20. Viola, Marco; Sangiovanni, Mara; Toraldo, Gerardo; Guarracino, Mario R.: A generalized eigenvalues classifier with embedded feature selection (2017)

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