E. Gibson, Perceptual Learning, Annual Review of Psychology, vol.14, issue.1, pp.29-56, 1963.
DOI : 10.1146/annurev.ps.14.020163.000333

A. Giordana and L. Saitta, Abstraction: a general framework for learning, Working notes of the AAAI Workshop on Automated Generation of Approximations and Abstraction, pp.245-256, 1990.

L. Saitta and J. Zucker, Semantic abstraction for concept representation and learning, Symposium on Abstraction, Reformulation and Approximation (SARA98), S. i. P. b. AAAI) Asilomar Conference Center, 1998.

J. Zucker, Changements de reprsentation, abstractions et apprentissages Mmoire d'habilitation diriger des recherches, 2001.

A. Giordana and L. Saitta, Phase transitions in relational learning, Machine Learning, pp.217-251, 2000.

I. Gent and T. Walsh, The TSP phase transition, Artificial Intelligence, vol.88, issue.1-2, pp.349-358, 1996.
DOI : 10.1016/S0004-3702(96)00030-6

I. P. Gent and T. Walsh, Phase transitions from real computational problems, Proceedings of the 8th International Symposium on Artificial Intelligence, pp.356-364, 1995.

P. Utgoff, Machine Learning: An Artificial Intelligence Approach, 1986.

S. Edelman and N. Intrator, Shift of bias for inductive concept learning, 2002.

B. Zenger and D. Savi, Plasticity of low-level visual networks, 2002.

R. L. Goldstone, Perceptual learning, Annual Reviews of Psychology, pp.585-612, 1998.
DOI : 10.1111/j.1532-7078.2010.00054.x

Y. Weiss, S. Edelman, and M. Fahle, Models of Perceptual Learning in Vernier Hyperacuity, Neural Computation, vol.287, issue.5, pp.695-718, 1993.
DOI : 10.1364/JOSAA.1.000124

M. Herzog and M. Fahle, Top-down information and models of perceptual learning, 2002.

V. Bruce and M. Burton, Learning new faces, 2002.

G. Wallis and H. Bulthoff, Learning to recognize objects, Trends in Cognitive Sciences, vol.3, issue.1, 2002.
DOI : 10.1016/S1364-6613(98)01261-3

E. Sacerdoti, Planning in a hierarchy of abstraction spaces, Artificial Intelligence, vol.5, issue.2, pp.115-135, 1974.
DOI : 10.1016/0004-3702(74)90026-5

P. Nayak and A. Levy, A semantic theory of abstraction, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-95), pp.196-192, 1995.

F. Giunchiglia, Using abstrips abstractions : Where do we stand ?, Artificial Intelligence Review, vol.13, issue.3, pp.201-213, 1996.
DOI : 10.1023/A:1006507609248

L. Saitta and J. Zucker, A model of abstraction in visual perception, Applied Artificial Intelligence, vol.19, issue.8, pp.761-776, 2001.
DOI : 10.1023/A:1022622132310
URL : https://hal.archives-ouvertes.fr/hal-01184259

R. Kohavi and D. Sommerfield, Feature subset selection using the wrapper method: Overfitting and dynamic search space topology, International Conference on Knowledge Discovery and Data Mining, 1995.

G. John, R. Kohavi, and K. Pfleger, Irrelevant Features and the Subset Selection Problem, Proceedings of the International Conference on Machile Learning, pp.121-129, 1994.
DOI : 10.1016/B978-1-55860-335-6.50023-4

J. Wnek and R. Muchalski, Hypothesis-driven constructive induction in aq17-hci -a method and experiments, Machine Learning, pp.139-168, 1994.

A. Blum and P. Langley, Selection of relevant features and examples in machine learning, Artificial Intelligence, pp.245-271, 1997.
DOI : 10.1016/S0004-3702(97)00063-5

J. Zucker and J. Ganascia, Changes of representation for efficient learning in structural domains, International Conference in Machine Learning, 1996.

N. Bredèche, Y. Chevaleyre, J. Zucker, A. Drogoul, and G. Sabah, A meta-learning approach to ground symbols from visual percepts Elsevier's Robotics and Autonomous Systems journal, special issue on Anchoring Symbols to Sensor Data in Single and Multiple Robot Systems, 2003.

L. Hugues and A. Drogoul, Pixel-based behavior learning, Proceedings of the 15th European Conference on Artificial Intelligence (ECAI'02), 2002.

R. Kohavi and G. John, The wrapper approach, " in Feature Selection for Knowledge Discovery and Data Mining, pp.33-50, 1998.

E. Baum, A Proposal for More Powerful Learning Algorithms, Neural Computation, vol.334, issue.2, pp.201-207, 1989.
DOI : 10.1145/322123.322138

S. E. Fahlman and C. Lebiere, The cascade-correlation learning architecture, Advances in Neural Information Processing Systems, Fig. 12. Percepts achieved thanks to the Unitization and Differentiation meta-operatos target concept is human

S. Coradeschi and A. Saffiotti, Anchoring symbolic object description to sensor data. problem statement, Linkping Electronic Articles in Computer and Information Science ISSN 1401-9841, 1999.

S. Harnad, The symbol grounding problem, pp.335-346, 1990.

Y. Chevaleyre and J. Zucker, A Framework for Learning Rules from Multiple Instance Data, Proc. European Conference on Machine Learning, 2001.
DOI : 10.1007/3-540-44795-4_5