THE MNIST DATABASE of handwritten digits. The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

References in zbMATH (referenced in 57 articles )

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  1. De Sterck, Hans; Howse, Alexander: Nonlinearly preconditioned optimization on Grassmann manifolds for computing approximate Tucker tensor decompositions (2016)
  2. Toutain, Matthieu; Elmoataz, Abderrahim; Lozes, François; Mansouri, Alamin: Non-local discrete $\infty $-Poisson and Hamilton Jacobi equations. From stochastic game to generalized distances on images, meshes, and point clouds (2016)
  3. Belilovsky, Eugene; Argyriou, Andreas; Varoquaux, Gaël; Blaschko, Matthew: Convex relaxations of penalties for sparse correlated variables with bounded total variation (2015)
  4. Curtin, Ryan R.; Lee, Dongryeol; March, William B.; Ram, Parikshit: Plug-and-play dual-tree algorithm runtime analysis (2015)
  5. Douc, Randal; Maire, Florian; Olsson, Jimmy: On the use of Markov chain Monte Carlo methods for the sampling of mixture models: a statistical perspective (2015)
  6. Germain, Pascal; Lacasse, Alexandre; Laviolette, Francois; Marchand, Mario; Roy, Jean-Francis: Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm (2015)
  7. Merkurjev, Ekaterina; Bae, Egil; Bertozzi, Andrea L.; Tai, Xue-Cheng: Global binary optimization on graphs for classification of high-dimensional data (2015)
  8. Zhang, Hai-Bin; Jiang, Jiao-Jiao; Zhao, Yun-Bin: On the proximal Landweber Newton method for a class of nonsmooth convex problems (2015)
  9. Adam, S.P.; Karras, D.A.; Magoulas, G.D.; Vrahatis, M.N.: Solving the linear interval tolerance problem for weight initialization of neural networks (2014)
  10. Daqi, Gao; Jun, Ding; Changming, Zhu: Integrated Fisher linear discriminants: an empirical study (2014)
  11. Feng, Bo-Yuan; Ren, Mingwu; Zhang, Xu-Yao; Suen, Ching Y.: Automatic recognition of serial numbers in bank notes (2014)
  12. Hansen, Toke J.; Mahoney, Michael W.: Semi-supervised eigenvectors for large-scale locally-biased learning (2014)
  13. Liao, Liang; Zhang, Yanning; John Maybank, Stephen; Liu, Zhoufeng: Intrinsic dimension estimation via nearest constrained subspace classifier (2014)
  14. Merkurjev, Ekaterina; Garcia-Cardona, Cristina; Bertozzi, Andrea L.; Flenner, Arjuna; Percus, Allon G.: Diffuse interface methods for multiclass segmentation of high-dimensional data (2014)
  15. Wang, Guan-Wei; Zhang, Chun-Xia; Zhuang, Jian: Clustering with prim’s sequential representation of minimum spanning tree (2014)
  16. Afkanpour, Arash; Szepesvári, Csaba; Bowling, Michael: Alignment based kernel learning with a continuous set of base kernels (2013)
  17. Fawzi, Alhussein; Frossard, Pascal: Image registration with sparse approximations in parametric dictionaries (2013)
  18. Shang, Fanhua; Jiao, L.C.; Liu, Yuanyuan; Tong, Hanghang: Semi-supervised learning with nuclear norm regularization (2013)
  19. Tapson, J.; van Schaik, A.: Learning the pseudoinverse solution to network weights (2013)
  20. Thom, Markus; Palm, Günther: Sparse activity and sparse connectivity in supervised learning (2013)

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