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 1830 articles )

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  1. Alabdulmohsin, Ibrahim; Cisse, Moustapha; Gao, Xin; Zhang, Xiangliang: Large margin classification with indefinite similarities (2016)
  2. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  3. Bai, Yan-Qin; Shen, Kai-Ji: Alternating direction method of multipliers for $\ell_1$-$\ell_2$-regularized logistic regression model (2016)
  4. Bertozzi, Andrea L.; Flenner, Arjuna: Diffuse interface models on graphs for classification of high dimensional data (2016)
  5. Bertsimas, Dimitris; King, Angela: OR forum: An algorithmic approach to linear regression (2016)
  6. Blaser, Rico; Fryzlewicz, Piotr: Random rotation ensembles (2016)
  7. Byrd, Richard H.; Chin, Gillian M.; Nocedal, Jorge; Oztoprak, Figen: A family of second-order methods for convex $\ell _1$-regularized optimization (2016)
  8. Caserta, Marco; Reiners, Torsten: A pool-based pattern generation algorithm for logical analysis of data with automatic fine-tuning (2016)
  9. de Campos, Cassio P.; Corani, Giorgio; Scanagatta, Mauro; Cuccu, Marco; Zaffalon, Marco: Learning extended tree augmented naive structures (2016)
  10. Eskandari, S.; Javidi, M.M.: Online streaming feature selection using rough sets (2016)
  11. Fung, Glenn M.; Mangasarian, Olvi L.: Unsupervised and semisupervised classification via absolute value inequalities (2016)
  12. Gondzio, Jacek; González-Brevis, Pablo; Munari, Pedro: Large-scale optimization with the primal-dual column generation method (2016)
  13. Goudie, Robert J.B.; Mukherjee, Sach: A Gibbs sampler for learning DAGs (2016)
  14. Jiang, Sheng-yi; Wang, Lian-xi: Efficient feature selection based on correlation measure between continuous and discrete features (2016)
  15. Kowaliw, Taras; Doursat, René: Bias-variance decomposition in genetic programming (2016)
  16. Kpotufe, Samory; Boularias, Abdeslam; Schultz, Thomas; Kim, Kyoungok: Gradients weights improve regression and classification (2016)
  17. Kuhnt, Sonja; Rehage, André: An angle-based multivariate functional pseudo-depth for shape outlier detection (2016)
  18. Kumar Jha, Govind; Kumar, Neeraj; Ranjan, Prabhat; Sharma, K.G.: Density based outlier detection (DBOD) in data mining: a novel approach (2016)
  19. Kurama, Onesfole; Luukka, Pasi; Collan, Mikael: An $n$-ary $\lambda$-averaging based similarity classifier (2016)
  20. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)

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