D. W. Aha, D. Kibler, and M. K. Albert, Instance-based learning algorithms, Machine Learning, vol.57, issue.1, pp.1-1, 1991.
DOI : 10.1007/BF00153759

A. Almaksour and E. Anquetil, Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition, 2009 10th International Conference on Document Analysis and Recognition, pp.81-85, 2009.
DOI : 10.1109/ICDAR.2009.23

URL : https://hal.archives-ouvertes.fr/hal-00491335

A. Almaksour, H. Mouchère, E. Anquetil, and . Fast, Online Incremental Learning with Few Examples For Online Handwritten Character Recognition, Proceedings of the Eleventh International Conference on Frontiers in Handwriting Recognition (ICFHR'08), pp.623-628, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00463233

P. Angelov and D. Filev, An Approach to Online Identification of Takagi-Sugeno Fuzzy Models, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.1-1, 2004.
DOI : 10.1109/TSMCB.2003.817053

G. A. Carpenter and S. Grossberg, The ART of adaptive pattern recognition by a self-organizing neural network, Computer, vol.21, issue.3, pp.3-3, 1988.
DOI : 10.1109/2.33

G. Carpenter, S. Grossberg, N. Markuzon, J. Reynolds, and D. Rosen, Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Transactions on Neural Networks, vol.3, issue.5, 1992.
DOI : 10.1109/72.159059

K. Chen and S. Wang, « Semi-supervised Learning via Regularized Boosting Working on Multiple Semi-supervised Assumptions, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.

F. Chung and T. Lee, Fuzzy competitive learning, Fuzzy Competitive Learning, pp.539-551, 1994.
DOI : 10.1016/0893-6080(94)90111-2

D. Backer, S. Scheunders, and P. , Texture segmentation by frequency-sensitive elliptical competitive learning, Proceedings 10th International Conference on Image Analysis and Processing, pp.9-9, 2001.
DOI : 10.1109/ICIAP.1999.797572

G. Gary and P. M. Yen, « An effective neuro-fuzzy paradigm for machinery condition health monitoring, IEEE Transactions on Systems, Man, and Cybernetics, pp.31-35, 2001.

J. Jang and «. Anfis, adaptive-network-based fuzzy inference system », Systems, Man and Cybernetics, IEEE Transactions on, vol.23, pp.665-685, 1993.
DOI : 10.1109/21.256541

. J. Jr and R. C. Zeleznik, « A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, pp.11-11, 2007.

N. Littlestone, Redundant Noisy Attributes, Attribute Errors, and Linear-threshold Learning Using Winnow, COLT '91 : Proceedings of the fourth annual workshop on Computational learning theory, pp.147-156, 1991.
DOI : 10.1016/B978-1-55860-213-7.50017-1

N. Littlestone and M. K. Warmuth, The Weighted Majority Algorithm, Information and Computation, vol.108, issue.2, pp.212-261, 1994.
DOI : 10.1006/inco.1994.1009

URL : http://doi.org/10.1006/inco.1994.1009

E. Lughofer and «. Flexfis, A Robust Incremental Learning Approach for Evolving TakagiSugeno Fuzzy Models », Fuzzy Systems, IEEE Transactions on, vol.16, issue.6, pp.1393-1410, 2008.

E. Lughofer, P. Angelov, and X. Zhou, « Evolving Single-And Multi-Model Fuzzy Classifiers with FLEXFIS-Class », Fuzzy Systems Conference, IEEE International, pp.1-6, 2007.

H. Mouchere and E. Anquetil, « Synthèse de caractères manuscrits en-ligne pour la reconnaissance de l'écriture », Actes du Colloque International Francophone sur l'Ecrit et le Document, pp.187-192, 2006.

H. Mouchere, E. Anquetil, and N. Ragot, On-line writer adaptation for handwriting recognition using fuzzy inference systems, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), pp.99-116, 2007.
DOI : 10.1109/ICDAR.2005.176

URL : https://hal.archives-ouvertes.fr/inria-00001246

G. Moustakides, Study of the transient phase of the forgetting factor RLS, IEEE Transactions on Signal Processing, vol.45, issue.10, pp.2468-2476, 1997.
DOI : 10.1109/78.640712

R. Polikar, L. Udpa, S. Udpa, V. Honavar, and . Learn++, Learn++: an incremental learning algorithm for supervised neural networks, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.31, issue.4, pp.497-508, 2001.
DOI : 10.1109/5326.983933

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

R. Reinke and R. Michalski, Incremental learning of concept descriptions : A method and experimental results, Machine Intelligence, p.263288, 1988.

J. Sadri, C. Y. Suen, and T. D. Bui, A New Clustering Method for Improving Plasticity and Stability in Handwritten Character Recognition Systems, 18th International Conference on Pattern Recognition (ICPR'06), pp.1130-1133, 2006.
DOI : 10.1109/ICPR.2006.114

J. P. Siebert, Vehicle Recognition Using Rule Based Methods, 1987.

T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, vol.15, issue.1, pp.1-1, 1985.
DOI : 10.1109/TSMC.1985.6313399

R. R. Yager and D. P. Fileu, « Learning of fuzzy rules by mountain clustering, SPIE, vol.2061, pp.246-254, 1993.