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 2621 articles )

Showing results 1 to 20 of 2621.
Sorted by year (citations)

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  1. Agor, Joseph; Özaltın, Osman Y.: Feature selection for classification models via bilevel optimization (2019)
  2. Alfonso Iodice D’Enza, Angelos Markos, Michel van de Velden: Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R (2019) not zbMATH
  3. Baumann, P.; Hochbaum, D. S.; Yang, Y. T.: A comparative study of the leading machine learning techniques and two new optimization algorithms (2019)
  4. Benítez-Peña, S.; Blanquero, R.; Carrizosa, E.; Ramírez-Cobo, P.: Cost-sensitive feature selection for support vector machines (2019)
  5. Borowik, Grzegorz: Optimization on the complementation procedure towards efficient implementation of the index generation function (2019)
  6. Boullé, Marc; Charnay, Clément; Lachiche, Nicolas: A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data (2019)
  7. Cherubin, Giovanni: Majority vote ensembles of conformal predictors (2019)
  8. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  9. Devarakonda, Aditya; Fountoulakis, Kimon; Demmel, James; Mahoney, Michael W.: Avoiding communication in primal and dual block coordinate descent methods (2019)
  10. Dey, Tamal K.; Shi, Dayu; Wang, Yusu: SimBa: an efficient tool for approximating Rips-filtration persistence via Simplicial Batch collapse (2019)
  11. Dubitzky, Werner; Lopes, Philippe; Davis, Jesse; Berrar, Daniel: The Open International Soccer Database for machine learning (2019)
  12. Dvořák, Jakub: Classification trees with soft splits optimized for ranking (2019)
  13. Evangelopoulos, Xenophon; Brockmeier, Austin J.; Mu, Tingting; Goulermas, John Y.: Continuation methods for approximate large scale object sequencing (2019)
  14. Gao, Can; Lai, Zhihui; Zhou, Jie; Wen, Jiajun; Wong, Wai Keung: Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction (2019)
  15. Hamidzadeh, Javad; Namaei, Neda: Belief-based chaotic algorithm for support vector data description (2019)
  16. Hsu, Hsiang-Ling; Chang, Yuan-chin Ivan; Chen, Ray-Bing: Greedy active learning algorithm for logistic regression models (2019)
  17. Hu, Yueqin; Treinen, Raymond: A one-step method for modelling longitudinal data with differential equations (2019)
  18. Johansson, Ulf; Löfström, Tuve; Linusson, Henrik; Boström, Henrik: Efficient Venn predictors using random forests (2019)
  19. Kantavat, Pittipol; Kijsirikul, Boonserm; Songsiri, Patoomsiri; Fukui, Ken-Ichi; Numao, Masayuki: Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation (2019)
  20. Karaca, Yeliz; Cattani, Carlo: Computational methods for data analysis (2019)

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