J. Denzler and C. M. Brown, Information theoretic sensor data selection for active object recognition and state estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.2, pp.145-157, 2002.
DOI : 10.1109/34.982896
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.408.4150

F. Callari and F. Ferrie, Autonomous recognition: driven by ambiguity, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.701-707, 1996.
DOI : 10.1109/CVPR.1996.517149

T. Arbel and F. P. Ferrie, Viewpoint selection by navigation through entropy maps, Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999.
DOI : 10.1109/ICCV.1999.791227

L. Paletta and A. Pinz, Active object recognition by view integration and reinforcement learning, Robotics and Autonomous Systems, vol.31, issue.1-2, pp.71-86, 2000.
DOI : 10.1016/S0921-8890(99)00079-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.5843

F. Deinzer, C. Derichs, H. Niemann, and J. Denzler, Integrated Viewpoint Fusion and Viewpoint Selection for Optimal Object Recognition, Procedings of the British Machine Vision Conference 2006, p.287, 2006.
DOI : 10.5244/C.20.30

S. D. Whitehead and D. H. Ballard, Learning to perceive and act by trial and error, Machine Learning, pp.45-83, 1991.
DOI : 10.1007/BF00058926

R. Martinez-cantin, N. De-freitas, E. Brochu, J. Castellanos, and A. Doucet, A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot, Autonomous Robots, vol.44, issue.3, pp.93-103, 2009.
DOI : 10.1007/s10514-009-9130-2

C. Watkins and P. Dayan, Q-learning, Machine learning, vol.8, issue.3, pp.279-292, 1992.

F. Deinzer, J. Denzler, and H. Niemann, Classifier Independent Viewpoint Selection for 3-D Object Recognition, Mustererkennung, vol.22, pp.237-244, 2000.
DOI : 10.1007/978-3-642-59802-9_30
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.432.5676

G. Matheron, Principles of geostatistics, Economic Geology, vol.58, issue.8, p.1246, 1963.
DOI : 10.2113/gsecongeo.58.8.1246

J. Lefebvre, H. Roussel, E. Walter, D. Lecointe, and W. Tabbara, Prediction from wrong models: the Kriging approach, IEEE Antennas and Propagation Magazine, vol.38, issue.4, pp.35-45, 1996.
DOI : 10.1109/74.537364

D. Jones, M. Schonlau, and W. Welch, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.
DOI : 10.1023/A:1008306431147

M. Sasena, Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations, 2002.

D. Jones, C. Perttunen, and B. Stuckman, Lipschitzian optimization without the Lipschitz constant, Journal of Optimization Theory and Applications, vol.20, issue.1, pp.157-181, 1993.
DOI : 10.1007/BF00941892

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1615-1630, 2005.
DOI : 10.1109/TPAMI.2005.188
URL : https://hal.archives-ouvertes.fr/inria-00548227