1. bookVolumen 4 (2014): Edición 1 (January 2014)
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eISSN
2449-6499
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30 Dec 2014
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The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks

Publicado en línea: 30 Dec 2014
Volumen & Edición: Volumen 4 (2014) - Edición 1 (January 2014)
Páginas: 5 - 19
Detalles de la revista
License
Formato
Revista
eISSN
2449-6499
Primera edición
30 Dec 2014
Calendario de la edición
4 veces al año
Idiomas
Inglés

[1] D. H. Ackley, G. E. Hinton, and T. J. Sejnowski. A learning algorithm for boltzmann machines. Cognitive Science, 9:147-169, 1985.10.1207/s15516709cog0901_7Search in Google Scholar

[2] Y. Bengio. Learning deep architectures for ai. Foundations and Trends in Machine Learning, 2(1):1-127, 2009.10.1561/2200000006Search in Google Scholar

[3] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.10.1145/1961189.1961199Search in Google Scholar

[4] Joseph Lin Chu and Adam Krzy˙zak. Application of support vector machines, convolutional neural networks and deep belief networks to recognition of partially occluded objects. In L. Rutkowski, editor, The 13th International Conference on Artificial Intelligence and Soft Computing ICAISC 2014, Lecture Notes on Artifical Intelligece (LNAI), volume 8467, pages 34-46. Springer International Publishing Switzerland, 2014.10.1007/978-3-319-07173-2_4Search in Google Scholar

[5] R. Collobert and S. Bengio. Links between perceptrons, mlps and svms. Proceedings of the 21st International Conference on Machine Learning, page 23, 2004.10.1145/1015330.1015415Search in Google Scholar

[6] C. Cortes and V. N. Vapnik. Support-vector networks. Machine Learning, 20:273-297, 1995.10.1007/BF00994018Search in Google Scholar

[7] K. Fukushima. Neocognitron for handwritten digit recognition. Neurocomputing, 51:161-180, 2003.10.1016/S0925-2312(02)00614-8Search in Google Scholar

[8] Kunihiko Fukushima and Sei Miyake. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition, 15(6):455-469, 1982.Search in Google Scholar

[9] G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800, 2002.10.1162/08997660276012801812180402Search in Google Scholar

[10] G. E. Hinton. A practical guide to training restricted boltzmann machines. Momentum, 9(1):599-619, 2010.10.1007/978-3-642-35289-8_32Search in Google Scholar

[11] G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554, 2006.10.1162/neco.2006.18.7.152716764513Search in Google Scholar

[12] G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313:504-507, 2006.10.1126/science.112764716873662Search in Google Scholar

[13] J. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the USA, 79(8):2554-2558, 1982.10.1073/pnas.79.8.25543462386953413Search in Google Scholar

[14] F. J. Huang and Y. LeCun. Large-scale learning with svm and convolutional nets for generic object categorization. Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1:284-291, 2006.Search in Google Scholar

[15] D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in a cat’s visual cortex. Journal of Physiology (London), 160:106-154, 1962.10.1113/jphysiol.1962.sp006837135952314449617Search in Google Scholar

[16] Y. LeCun, L. Bottou, Y. Bengio, and Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.10.1109/5.726791Search in Google Scholar

[17] Y. LeCun, F.J. Huang, and L. Bottou. Learning methods for generic object recognition with invariance to pose and lighting. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2:97-104, 2004.Search in Google Scholar

[18] Aleix M. Mart´ınez. Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6):748-763, 2002.10.1109/TPAMI.2002.1008382Search in Google Scholar

[19] V. Nair and G. E. Hinton. 3d object recognition with deep belief nets. Advances in Neural Information Processing Systems (NIPS), pages 1339-1347, 2009.Search in Google Scholar

[20] M. Ranzato, J. Susskind, V. Mnih, and G. Hinton. On deep generative models with applications to recognition. 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2857-2864, 2011.Search in Google Scholar

[21] M. A. Ranzato, F. J. Huang, Y. L. Boureau, and Y. LeCun. Unsupervised learning of invariant feature hierarchies with applications to object recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, 2007.Search in Google Scholar

[22] P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. In David E. Rumelhart and James L. McLelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1, chapter 6, pages 194-281. MIT Press, 1986.Search in Google Scholar

[23] P.W. M. Tsang and P. C. Yuen. Recognition of partially occluded objects. IEEE Transactions on Systems, Man and Cybernetics, 23(1):228-236, 1993.10.1109/21.214781Search in Google Scholar

[24] John Winn and Jamie Shotton. The layout consistent random field for recognizing and segmenting partially occluded objects. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1:37-44, 2006.Search in Google Scholar

[25] LaurenzWiskott and Christoph Von Der Malsburg. A neural system for the recognition of partially occluded objects in cluttered scenes: A pilot study. International Journal of Pattern Recognition and Artificial Intelligence, 7(4):935-948, 1993 Search in Google Scholar

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