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 70 articles )

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  1. Charisopoulos, Vasileios; Maragos, Petros: Morphological perceptrons: geometry and training algorithms (2017)
  2. Cloninger, Alexander; Czaja, Wojciech; Doster, Timothy: The pre-image problem for Laplacian eigenmaps utilizing $L_1$ regularization with applications to data fusion (2017)
  3. Ding, Chao; Qi, Hou-Duo: Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction (2017)
  4. Dong, Bin: Sparse representation on graphs by tight wavelet frames and applications (2017)
  5. Elmoataz, Abderrahim; Lozes, François; Toutain, Matthieu: Nonlocal PDEs on graphs: from Tug-of-War games to unified interpolation on images and point clouds (2017)
  6. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  7. Haojin Yang, Martin Fritzsche, Christian Bartz, Christoph Meinel: BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet (2017) arXiv
  8. Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth: sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo (2017) arXiv
  9. Merkurjev, Ekaterina; Bertozzi, Andrea; Yan, Xiaoran; Lerman, Kristina: Modified Cheeger and ratio cut methods using the Ginzburg-Landau functional for classification of high-dimensional data (2017)
  10. Shi, Zuoqiang; Osher, Stanley; Zhu, Wei: Weighted nonlocal Laplacian on interpolation from sparse data (2017)
  11. Chmielnicki, Wiesław; Stąpor, Katarzyna: Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification (2016)
  12. Couillet, Romain; Benaych-Georges, Florent: Kernel spectral clustering of large dimensional data (2016)
  13. De Sterck, Hans; Howse, Alexander: Nonlinearly preconditioned optimization on Grassmann manifolds for computing approximate Tucker tensor decompositions (2016)
  14. 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)
  15. Zhou, Yunkai; Wang, Zheng; Zhou, Aihui: Accelerating large partial EVD/SVD calculations by filtered block Davidson methods (2016)
  16. Belilovsky, Eugene; Argyriou, Andreas; Varoquaux, Gaël; Blaschko, Matthew: Convex relaxations of penalties for sparse correlated variables with bounded total variation (2015)
  17. Curtin, Ryan R.; Lee, Dongryeol; March, William B.; Ram, Parikshit: Plug-and-play dual-tree algorithm runtime analysis (2015)
  18. 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)
  19. 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)
  20. Merkurjev, Ekaterina; Bae, Egil; Bertozzi, Andrea L.; Tai, Xue-Cheng: Global binary optimization on graphs for classification of high-dimensional data (2015)

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