[1] Dollár P, Tu Z, Perona P, et al. Integral Channel Features [C]// British Machine Vision Conference BMVC 2009, London, UK, Sep 7-10, 2009, 2(3): 5.
[2] SU Songzhi, LI Shaozi, CHEN Shuyuan, et al. A Survey on Pedestrian Detection [J]. Acta Electronica Sinica, 2012, 40(4): 814-820.
[3] Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models [J]. Pattern Analysis and Machine Intelligence(S0162-8828), 2010, 32(9): 1627-1645.
[4] Maji S, Berg A C, Malik J. Efficient classification for additive kernel SVMs [J]. Pattern Analysis and Machine Intelligence(S0162-8828), 2013, 35(1): 66-77.
[5] Dollár P, Appel R, Kienzle W, et al. Crosstalk cascades for frame-rate pedestrian detection [C]// Computer Vision–ECCV, Springer Berlin Heidelberg, 2012: 645-659.
[6] Abdel-hamid O, Mohamed A, JIANG Hui, et al. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition [C]// 2012 IEEE International Conference on, Kyoto, March 25-30, 2012: 4277-4280.
[7] Hinton G E, Osindero S, Teh YW, et al. A fast learning algorithm for deep belief nets [J]. Neural computation(S0899-7667), 2006, 18(7): 1527-1554.
[8] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks [J]. Science(S0036-8075), 2006, 313(5786): 504-507.
[9] Bengio Y, Lamblin P, Popovici D, et al. Greedy layer-wise training of deep networks [J]. Advances in Neural Information Processing Systems(S1049-5258), 2007, 19: 153.
[10] Vidya R, Nasira G M, Priyankka R P. Sparse Coding: A Deep Learning using Unlabeled Data for High-Level Representation[C]// Computing and Communication Technologies, Trichirappalli, Feb 27-March 1, 2014: 124-127.
[11] Coates A, Lee H, Ng A Y. An analysis of single-layer networks in unsupervised feature learning [C]// International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, USA, Apr 11-13, 2011, 15: 215-223.
[12] Ranzato M, Poultney C, Chopra S, et al. Efficient learning of sparse representations with an energy-based model [C]// Advances in Neural Information Processing Systems 19, Vancouver, B C, Canada, Dec 4-9, 2006: 1137-1144.
[13] Pedroni B U, Das S, Neftci E, et al. Neuromorphic adaptations of restricted Boltzmann machines and deep belief networks [C]// The 2013 International Joint Conference on, Dallas, T X, Aug 4-9, 2013: 1-6.
[15] Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems [J]. SIAM Journal on Imaging Sciences(S1936-4954), 2009, 2(1): 183-202.
