H. Barlow, H. Bay, T. Tuytelaars, and L. V. , Redundancy reduction revisited, Proceedings of the 9 th European conference on Computer Vision, pp.241-325, 2001.
DOI : 10.1147/rd.42.0208

A. J. Bell and T. J. Sejnowski, The ???independent components??? of natural scenes are edge filters, Bengio. Learning deep architectures for ai Machine Learning, pp.3327-33381, 1997.
DOI : 10.1016/S0042-6989(97)00121-1

Y. Bengio and Y. Lecun, Scaling learning algorithms towards ai, Large Scale Kernel Machines

Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, Greedy layer-wise training of deep networks, Proceedings of the Advances in Neural Information Processing Systems (NIPS 2007), pp.153-160, 2007.

K. P. Bennett and A. Demiriz, Semi-supervised support vector machines, Proceedings of the Advances in Neural Information Processing Systems (NIPS 1998), pp.368-374

K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, When Is ???Nearest Neighbor??? Meaningful?, Proceedings of the International Conferecne on Database Theory, pp.217-235, 1999.
DOI : 10.1007/3-540-49257-7_15

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

P. Blaer and P. Allen, Topological mobile robot localization using fast vision techniques, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), pp.1031-1036, 2002.
DOI : 10.1109/ROBOT.2002.1013491

A. Blum and T. M. Mitchel, Combining labeled and unlabeled data with cotraining, Proceedings of the 11 th Annual Conference on Learning Theory, pp.92-100, 1998.

J. Borenstein, H. R. Everett, L. Feng, and D. Wehe, Mobile robot positioning: Sensors and techniques, Journal of Robotic Systems, vol.14, issue.4, pp.231-249, 1997.
DOI : 10.1002/(SICI)1097-4563(199704)14:4<231::AID-ROB2>3.0.CO;2-R

Y. Boureau, F. Bach, Y. Lecun, and J. Ponce, Learning mid-level features for recognition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2559-2566, 2010.
DOI : 10.1109/CVPR.2010.5539963

M. A. Carreira-perpinan and G. E. Hinton, On contrastive divergence learning, 2005.

C. Chang, D. L. Page, and M. A. Abidi, Object-based place recognition and loop closing with jigsaw puzzle image segmentation algorithm, 2008 IEEE International Conference on Robotics and Automation, pp.557-562, 2008.
DOI : 10.1109/ROBOT.2008.4543265

C. Chang, A. Koschan, and M. A. Abidi, Object-based place recognition and scene change detection for perimeter patrol, Journal of Transactions of the American Nuclear Society, vol.101, pp.823-824, 2009.

W. J. Christopher, Learning from delayed rewards King's College, 1989.

E. Doi, D. C. Balcan, and M. S. Lewicki, A theoretical analysis of robust coding over noisy overcomplete channels, Proceedings of the Advances in Neural Information Processing Systems (NIPS 2006, pp.307-314, 2006.

A. Doucet, On sequential simulation-based methods for bayesian filtering, Signal Processing Group, 1998.

M. Dubois, H. Guillaume, E. Frenoux, P. Tarroux, P. A. Manzagol et al., Visual place recognition using bayesian filtering with markov chains The difficulty of training deep architectures and the effect of unsupervised pre-training, Proceedings of the 19 th European Symposium on Artificial Neural Networks Journal of Machine Learning Research -Proceedings Track, pp.435-440153, 2009.

R. Fergus, Visual object category recognition, 2005.

D. J. Field, Relations between the statistics of natural images and the response properties of cortical cells What is the goal of sensory coding, Journal of Optical Society of America Journal of Neural Computation, vol.6, issue.4124, pp.2379-2394, 1987.

D. Filliat, Interactive learning of visual topological navigation Empirical analysis of the divergence of gibbs sampling based learning algorithms for restricted boltzmann machines, Proceedings of the IEEE International Conference on Intelligent Robots and Systems Proceedings of the 20 th international conference on Artificial neural networks, pp.248-254, 2008.

Y. Freund and D. Haussler, Unsupervised learning of distributions on binary vectors using two layer networks, 1994.

B. H. Gary, R. Manu, B. Tamara, and L. M. Erik, Labeled faces in the wild: A database for studying face recognition in unconstrained environments, 2007.

J. Gaspar, N. Winters, J. Santos, and . Victor, Vision-based navigation and environmental representations with an omnidirectional camera, IEEE Transactions on Robotics and Automation, vol.16, issue.6, pp.890-898, 2000.
DOI : 10.1109/70.897802

S. Geman and D. Geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, Journal of IEEE Transactions Pattern Analysis and Machine Intelligence, vol.6, issue.6, pp.721-741, 1984.

D. Gokalp and S. Aksoy, Scene Classification Using Bag-of-Regions Representations, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383375

H. Guillaume, M. Dubois, E. Frenoux, and P. Tarroux, Temporal bag-of-words a generative model for visual place recognition using temporal integration Monte carlo techniques for prediction and filtering of non-linear stochastic processes, Proceedings of the 6 th International Conference on Computer Vision Theory and Applications, pp.286-295555, 1970.

J. Handschin and D. Mayne, Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering???, International Journal of Control, vol.83, issue.5, pp.547-559, 1969.
DOI : 10.1016/0022-0396(67)90023-X

R. Hecht-nielsen, Replicator Neural Networks for Universal Optimal Source Coding, Science, vol.269, issue.5232, pp.1860-1863, 1995.
DOI : 10.1126/science.269.5232.1860

G. E. Hinton and G. E. Hinton, Training Products of Experts by Minimizing Contrastive Divergence, Deep belief networks, pp.1771-180047, 2002.
DOI : 10.1162/089976600300015385

G. E. Hinton, A practical guide to training restricted boltzmann machines -version 1, 2010.

E. Hinton and R. R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.504-507, 2006.
DOI : 10.1126/science.1127647

G. E. Hinton, T. J. Sejnowski, and D. H. Ackley, Boltzmann machines: Constraint satisfaction networks that learn, 1984.

G. E. Hinton, S. Osindero, Y. Teh, G. E. Hinton, A. Krizhevsky et al., A Fast Learning Algorithm for Deep Belief Nets, Proceedings of the International Conference on Artificial Neural Networks (ICANN 2011) Proceedings of the National Academy of Sciences of the United States, pp.1527-1554, 1982.
DOI : 10.1162/jmlr.2003.4.7-8.1235

A. S. Hsu and T. L. Griffiths, Effects of generative and discriminative learning on use of category variability, Proceedings of the 32 nd Annual Conference of the Cognitive Science Society, 2010.

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, viii A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

N. Jaitly and G. E. Hinton, Learning a better representation of speech soundwaves using restricted boltzmann machines, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5884-5887, 2011.
DOI : 10.1109/ICASSP.2011.5947700

T. Joachims, Transductive inference for text classification using support vector machines, Proceedings of the 1999 International Conference on Machine Learning (ICML 1999), pp.200-209, 1999.

A. Krizhevsky-x, X. A. Krizhevsky, A. Krizhevsky, and G. E. Hinton, Learning multiple layers of features from tiny images Master science thesis Convolutional deep belief networks on cifar-10 Using very deep autoencoders for content-based image retrieval, Proceedings of the 19 th European Symposium on Artificial Neural Networks. xxvi S. Kullback and R. A. Leibler. On information and sufficiency. Journal of the Annals of Mathematical Statistics, pp.79-86, 1951.

K. Labusch and T. Martinetz, Learning sparse codes for image reconstruction, Proceedings of the 18 th European Symposium on Artificial Neural networks, Computational Intelligence and Machine Learning

Y. Larochelle, J. Bengio, P. Louradour, and . Lamblin, Exploring strategies for training deep neural networks, Journal of Machine Learning Research, vol.1, pp.1-40, 2009.

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2169-2178, 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

Y. Lecun, S. Chopra, R. Hadsell, M. A. Ranzato, and F. J. Huang, A tutorial on energy-based learning, Predicting Structured Data, 2006.

H. Lee, C. Ekanadham, A. Y. Ng, X. H. Lee, R. Grosse et al., Sparse deep belief net model for visual area v2 Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations Text categorization with support vector machines. how to represent texts in input space, Proceedings of the Advances in Neural Information Processing Systems Proceedings of the 26 th International Conference on Machine Learning, pp.873-880, 2002.

O. Linde and T. Lindeberg, Object recognition using composed receptive field histograms of higher dimensionality, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.1-6, 2004.
DOI : 10.1109/ICPR.2004.1333965

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999.
DOI : 10.1109/ICCV.1999.790410

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

J. Luo, A. Pronobis, B. Caputo, and P. Jensfelt, The kth-idol2 database, Royal Insitute of Technology, 2006.

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Supervised dictionary learning, Proceedings of the Advances in Neural Information Processing Systems (NIPS 2008, pp.1033-1040, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00322431

E. Menegatti, M. Zoccarato, E. Pagello, and H. Ishiguro, Image-based Monte Carlo localisation with omnidirectional images, Robotics and Autonomous Systems, vol.48, issue.1, pp.17-30, 2004.
DOI : 10.1016/j.robot.2004.05.003

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.2272

T. Mitchell, Machine Learning, McGraw Hill Series in Computer Science, 1997.

H. Murase and S. K. Nayar, Visual learning and recognition of 3-d objects from appearance, International Journal of Computer Vision, vol.37, issue.10, pp.5-24, 1995.
DOI : 10.1007/BF01421486

V. Nair and G. E. Hinton, Implicit mixtures of restricted boltzmann machines, Proceedings of the Advances in Neural Information Processing Systems (NIPS 2008), pp.1145-1152, 2008.

V. Nair and G. E. Hinton, 3-d object recognition with deep belief nets, Proceedings of the Advances in Neural Information Processing Systems, pp.1339-1347, 2009.

V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines

A. Y. Ng, Cs229 lecture notes on machine learning, 2011.

A. Y. Ng and M. I. Jordan, On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes, Proceedings of the Advances in Neural BIBLIOGRAPHY Information Processing Systems, pp.841-848, 2002.

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

M. Nosofsky, D. R. Little, and T. W. James, Activation in the neural network responsible for categorization and recognition reects parameter changes, Proceedings of the National Academy of Sciences, vol.109, issue.20, pp.91-110, 2011.

T. Ojala, M. Pietikainen, and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.24971-987, 2002.
DOI : 10.1109/TPAMI.2002.1017623

A. Oliva and A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, International Journal of Computer Vision, vol.42, issue.3, pp.145-175, 2001.
DOI : 10.1023/A:1011139631724

A. Oliva and A. Torralba, Chapter 2 Building the gist of a scene: the role of global image features in recognition, Journal of Progress in Brain Research, vol.155, pp.23-36, 2006.
DOI : 10.1016/S0079-6123(06)55002-2

B. A. Olshausen, D. J. Field, B. A. Olshausen, D. J. Field, B. A. Olshausen et al., Emergence of simple-cell receptive field properties by learning a sparse code for natural images Sparse coding with an overcomplete basis set: a strategy employed by v1 Sparse coding of sensory inputs, Journal of Nature Journal of Vision Research Journal of Current Opinion in Neurobiology, vol.381, issue.144, pp.607-6093311, 1996.

C. Papageorgiou and T. Poggio, A trainable system for object detection, International Journal of Computer Vision, vol.38, issue.1, pp.15-33, 2000.
DOI : 10.1023/A:1008162616689

M. Pazzani and P. Domingos, On the optimality of the simple bayesian classifier under zero-one loss, Journal of Machine learning, vol.29, issue.2-3, pp.103-130, 1997.

J. Peters, S. Vijayakumar, and S. Schaal, Reinforcement learning for humanoid robotics, Proceedings of the IEEE-RAS International Conference on Humanoid Robots, pp.1-20, 2003.

J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, Object retrieval with large vocabularies and fast spatial matching, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383172

A. Pronobis, Indoor place recognition using support vector machines, 2005.

A. Pronobis and B. Caputo, Confidence-base cue integration for visual place recognition, Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 2007.

A. Pronobis, B. Caputo, P. Jensfelt, H. I. Christensen, X. A. Pronobis et al., A discriminative approach to robust visual place recognition Multi-modal semantic place classification, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.3829-3836, 2006.

J. R. Quinlan, Induction of decision trees, Machine Learning, vol.1, issue.1, pp.81-106, 1986.
DOI : 10.1007/BF00116251

M. A. Ranzato, A. Krizhevsky, G. E. Hinton, D. E. Rumelhart, and J. L. Mcclelland, Factored 3-way restricted boltzmann machines for modeling natural images Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Journal of Machine Learning Research (JMLR) -Proceedings Track, vol.9, pp.621-628, 1986.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning Internal Representations by Error Propagation, Parallel Distributed Processing: explorations in the microstructure of cognition, pp.318-362, 1986.
DOI : 10.1016/B978-1-4832-1446-7.50035-2

R. Salakhutdinov, Learning in markov random fields using tempered transitions, Proceedings of the Advances in Neural Information Processing Systems, pp.1598-1606, 2009.

R. Salakhutdinov and G. E. Hinton, Semantic hashing, Proceedings of the SIGIR Workshop on Information Retrieval and Applications of Graphical Models, 2007.
DOI : 10.1016/j.ijar.2008.11.006

R. Salakhutdinov and G. E. Hinton, Deep boltzmann machines, Proceedings of the International Conference on Artificial Intelligence and Statistics, pp.448-455, 2009.

R. Salakhutdinov, A. Mnih, and G. E. Hinton, Restricted Boltzmann machines for collaborative filtering, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.791-798, 2007.
DOI : 10.1145/1273496.1273596

A. L. Samuel, Some Studies in Machine Learning Using the Game of Checkers, IBM Journal of Research and Development, vol.3, issue.3, pp.210-229, 1959.
DOI : 10.1147/rd.33.0210

R. Sarikaya, G. E. Hinton, and B. Ramabhadran, Deep belief nets for natural language call-routing, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5680-5683, 2011.
DOI : 10.1109/ICASSP.2011.5947649

H. Schneiderman and T. Kanade, A statistical model for 3d object detection applied to faces and cars, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2000.

H. Schulz, A. Müller, and S. Behnke, Investigating convergence of restricted boltzmann machine learning, Proceedings of NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning, 2010.

S. Se, D. G. Lowe, and J. Little, Vision-based mobile robot localization and mapping using scale-invariant features, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), pp.2051-2058, 2001.
DOI : 10.1109/ROBOT.2001.932909

T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, Robust Object Recognition with Cortex-Like Mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.3, pp.411-426, 2007.
DOI : 10.1109/TPAMI.2007.56

P. Smolensky, K. P. Soman, R. Loganathan, and V. Ajay, Information processing in dynamical systems: foundations of harmony theory machine learning with SVM and other kernel methods. PHI Learning Private Limited, M-97, Parallel Distributed Processing Explorations in the Micorstructure of Cognition viii M. Steinbach, G. Karypis, and V. Kumar. A comparison of document clustering techniques, 1986.

I. Sutskever, G. E. Hinton, and G. W. Taylor, The recurrent temporal restricted boltzmann machine, Proceedings of the Advances in Neural Information Processing Systems, pp.1601-1608, 2008.

G. W. Taylor and G. E. Hinton, Factored conditional restricted Boltzmann Machines for modeling motion style, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.129, 2009.
DOI : 10.1145/1553374.1553505

G. W. Taylor, G. E. Hinton, and S. Roweis, Modeling human motion using binary latent variables, Proceedings of the Advances in Neural Information Processing Systems (NIPS 2006), pp.1345-1352, 2006.

Y. W. Teh, M. Welling, S. Osindero, and G. E. Hinton, Energy-based models for sparse overcomplete representations, JMLR) - Proceedings Track, pp.4-7, 2003.

G. Tesauro, Practical issues in temporal difference learning, Journal of Machine Learning, vol.8, pp.257-277, 1992.

S. Thrun, Is robotics going statistics? the field of probabilistic robotics, Journal of Communications of the ACM, 2001.

S. Thrun, D. Fox, W. Burgard, and F. Dellaert, Robust Monte Carlo localization for mobile robots, Artificial Intelligence, vol.128, issue.1-2, pp.99-141, 2000.
DOI : 10.1016/S0004-3702(01)00069-8

S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), 2005.

A. Torralba, Contextual priming for object detection, International Journal of Computer Vision, vol.53, issue.2, pp.169-191, 2003.
DOI : 10.1023/A:1023052124951

A. Torralba, K. P. Murphy, W. T. Freeman, M. A. Rubin, K. P. Torralba et al., Context-based vision system for place and object recognition Context-based vision system for place and object recognition, Proceedings of the IEEE International Conference on Computer Vision Proceedings of the IEEE International Conference on Computer Vision, pp.273-280, 2003.

A. Torralba, R. Fergus, Y. Weiss, M. Turk, and A. Pentland, Small codes and large image databases for recognition Eigenfaces for recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-871, 1991.

M. M. Ullah, A. Pronobis, B. Caputo, J. Luo, P. Jensfelt et al., The cold database CAS -Centre for Autonomous Systems Towards robust place recognition for robot localization Appearance-based place recognition for topological localization, Proceedings of the IEEE International Conference on Robotics and Automation Proceedings of the IEEE International Conference on Robotics and Automation, pp.3829-3836, 2000.

I. Ulusoy and C. M. Bishop, Generative versus Discriminative Methods for Object Recognition, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.258-265, 2005.
DOI : 10.1109/CVPR.2005.167

V. N. Vapnik, The nature of statistical learning theory, 1995.

V. N. Vapnik, Statistical Learning Theory, 1998.

I. Vilares and K. Kording, Bayesian models: the structure of the world, uncertainty, behavior, and the brain, Annals of the New York Academy of Sciences, vol.2003, issue.1, p.1224, 2011.
DOI : 10.1111/j.1749-6632.2011.05965.x

P. Viola and M. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2002.
DOI : 10.1023/B:VISI.0000013087.49260.fb

C. Wallraven, B. Caputo, and A. B. Graf, Recognition with local features: the kernel recipe, Proceedings Ninth IEEE International Conference on Computer Vision, pp.257-264, 2003.
DOI : 10.1109/ICCV.2003.1238351

D. Walther and C. Koch, Modeling attention to salient proto-objects, Neural Networks, vol.19, issue.9, pp.1395-1407, 2006.
DOI : 10.1016/j.neunet.2006.10.001

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.1493

M. Welling, M. Rosen-zvi, and G. E. Hinton, Exponential family harmoniums with an application to information retrieval, Proceedings of the Advances in Neural Information Processing Systems (NIPS 2004), pp.1481-1488, 2004.

F. Wood and G. E. Hinton, Training products of experts by minimizing contrastive divergence, 2012.

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. S. Huang et al., Sparse Representation for Computer Vision and Pattern Recognition, Proceedings of the IEEE, pp.1031-1044, 2010.
DOI : 10.1109/JPROC.2010.2044470

J. Wu and J. M. Rehg, Centrist: A visual descriptor for scene categorization, Journal of IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.33, issue.8, pp.1489-1501, 2011.

J. Wu, H. I. Christensen, J. M. Rehg, . Ieee, J. Iv et al., Visual place categorization: Problem, dataset, and algorithm Linear spatial pyramid matching using sparse coding for image classification, Proceedings of the IEEE Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Icml 2009 workshop on learning feature hierarchies, pp.4763-4770, 2009.

R. Zabih and J. Woodfill, Non-parametric local transforms for computing visual correspondence, Proceedings of the third European conference on Computer Vision, pp.151-158, 1994.
DOI : 10.1007/BFb0028345