G. E. Hinton, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol.22, issue.8, pp.14-1771, 2002.
DOI : 10.1162/089976600300015385

N. Roux and Y. Bengio, Representational Power of Restricted Boltzmann Machines and Deep Belief Networks, Neural Computation, vol.20, issue.6, pp.1631-1649, 2008.
DOI : 10.1016/S0364-0213(85)80012-4

G. E. Hinton, S. Osindero, and Y. Teh, A Fast Learning Algorithm for Deep Belief Nets, Neural Computation, vol.18, issue.7, pp.18-1527, 2006.
DOI : 10.1162/jmlr.2003.4.7-8.1235

G. E. Hinton and R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.313504-507, 2006.
DOI : 10.1126/science.1127647

H. Lee, P. T. Pham, and L. Yan, Unsupervised feature learning for audio classification using convolutional deep belief networks, Advances in Neural Information Processing Systems, pp.1096-1104, 2009.

M. Norouzi, M. Ranjbar, and G. Mori, Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.2735-2742, 2009.
DOI : 10.1109/CVPR.2009.5206577

R. Salakhutdinov and H. Larochelle, Efficient Learning of Deep Boltzmann Machines. Efficient Learning of Deep Boltzmann Machines, Journal of Machine Learning Research, vol.2010, issue.98, pp.693-700

R. Salakhutdinov and G. E. Hinton, An Efficient Learning Procedure for Deep Boltzmann Machines, Neural Computation, vol.17, issue.8, pp.241967-2006
DOI : 10.1080/17442509908834179

J. Zhang, S. F. Ding, and N. Zhang, Incremental extreme learning machine based on deep feature embedded, International Journal of Machine Learning and Cybernetics, vol.17, issue.4, 2015.
DOI : 10.1109/CVPR.2009.5206577

N. Zhang, S. F. Ding, and Z. Z. Shi, Denoising Laplacian multi-layer extreme learning machine, Neurocomputing, vol.171, pp.1066-1074, 2016.
DOI : 10.1016/j.neucom.2015.07.058

S. F. Ding, N. Zhang, and X. Z. Xu, Deep Extreme Learning Machine and Its Application in EEG Classification, Mathematical Problems in Engineering, vol.10, issue.3, pp.1-11
DOI : 10.1038/18581

Y. Zheng, B. Jeon, D. Xu, and Q. M. , Image segmentation by generalized hierarchical fuzzy C-means algorithm, Journal of Intelligent and Fuzzy Systems, issue.2, pp.28-961, 2015.

N. Srivastava, G. E. Hinton, and A. Krizhevsky, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, vol.15, pp.1929-1958, 2014.

C. Blundell, J. Cornebise, and K. Kavukcuoglu, Weight uncertainty in Neural Networks, Proceedings of the 32nd International Conference on Machine Learning, 2015.

A. Krizhevsky and G. E. Hinton, Learning multiple layers of features from tiny images, 2009.

T. Tieleman, Training restricted Boltzmann machines using approximations to the likelihood gradient, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1064-1071, 2008.
DOI : 10.1145/1390156.1390290

T. Tieleman and G. E. Hinton, Using fast weights to improve persistent contrastive divergence, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1033-1040, 2009.
DOI : 10.1145/1553374.1553506