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 2798 articles )

Showing results 1 to 20 of 2798.
Sorted by year (citations)

1 2 3 ... 138 139 140 next

  1. Bertsimas, Dimitris; Cory-Wright, Ryan: On polyhedral and second-order cone decompositions of semidefinite optimization problems (2020)
  2. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  3. Borchmann, Daniel; Hanika, Tom; Obiedkov, Sergei: Probably approximately correct learning of Horn envelopes from queries (2020)
  4. Firat, Murat; Crognier, Guillaume; Gabor, Adriana F.; Hurkens, C. A. J.; Zhang, Yingqian: Column generation based heuristic for learning classification trees (2020)
  5. Gurevich, Pavel; Stuke, Hannes: Gradient conjugate priors and multi-layer neural networks (2020)
  6. Ibrahim, Mohamed-Hamza; Missaoui, Rokia: Approximating concept stability using variance reduction techniques (2020)
  7. Lopes, Miles E.: Estimating a sharp convergence bound for randomized ensembles (2020)
  8. Luo, Jian; Yan, Xin; Tian, Ye: Unsupervised quadratic surface support vector machine with application to credit risk assessment (2020)
  9. Malyscheff, Alexander M.; Trafalis, Theodore B.: Kernel classification using a linear programming approach (2020)
  10. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
  11. Roulet, Vincent; d’Aspremont, Alexandre: Sharpness, restart, and acceleration (2020)
  12. Wang, Di; Xu, Jinhui: Principal component analysis in the local differential privacy model (2020)
  13. Zhang, Yangjing; Zhang, Ning; Sun, Defeng; Toh, Kim-Chuan: An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems (2020)
  14. Adona, Vando A.; Gonçalves, Max L. N.; Melo, Jefferson G.: A partially inexact proximal alternating direction method of multipliers and its iteration-complexity analysis (2019)
  15. Agor, Joseph; Özaltın, Osman Y.: Feature selection for classification models via bilevel optimization (2019)
  16. Ahsen, Mehmet Eren; Vogel, Robert M.; Stolovitzky, Gustavo A.: Unsupervised evaluation and weighted aggregation of ranked classification predictions (2019)
  17. Alaya, Mokhtar Z.; Bussy, Simon; Gaïffas, Stéphane; Guilloux, Agathe: Binarsity: a penalization for one-hot encoded features in linear supervised learning (2019)
  18. Alfonso Iodice D’Enza, Angelos Markos, Michel van de Velden: Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R (2019) not zbMATH
  19. Armengol, Eva; Boixader, Dionís; García-Cerdaña, Àngel; Recasens, Jordi: (T)-generable indistinguishability operators and their use for feature selection and classification (2019)
  20. Artemiou, Andreas: Using adaptively weighted large margin classifiers for robust sufficient dimension reduction (2019)

1 2 3 ... 138 139 140 next