]. M. Boui-12, E. Bouillon, A. Anquetil, and . Almaksour, Decremental Learning of Evolving Fuzzy Inference Systems Using a Sliding Window, Proceedings of the 11th International Conference on Machine Learning and Applications (ICMLA), pp.598-601, 2012.

]. M. Boui-13a, E. Bouillon, A. Anquetil, and . Almaksour, Decremental Learning of Evolving Fuzzy Inference Systems, Application to Handwritten Gesture Recognition, Proceedings of the 9th International Conference on Machine Learning and Data Mining (MLDM), pp.115-129, 2013.

]. M. Boui-13b, E. Bouillon, A. Anquetil, and . Almaksour, Étude des techniques d'oubli dans les moindres carrés récursifs pour l'apprentissage incrémental de systèmes d'inférence floue évolutifs : application à la reconnaissance de formes, Actes de la 13e Conférence Francophone sur l'Extraction et la Gestion des Connaissances, pp.15-24, 2013.

]. M. Boui-13c, P. Bouillon, E. Li, G. Anquetil, and . Richard, Using Confusion Reject to Improve (User and) System (Cross) Learning of Gesture Commands, Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pp.1017-1021, 2013.

]. M. Boui-14a, E. Bouillon, and . Anquetil, Man-Machine Cooperation for the On-Line Training of an Evolving Classifier, Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp.1-7, 2014.

]. M. Boui-14b, E. Bouillon, and . Anquetil, Optimisation de la coopération utilisateur/système pour l'apprentissage en-ligne d'un classifieur évolutif, Actes du dix-neuvième congrès national sur la Reconnaissance de Formes et l'Intelligence Artificielle (RFIA), 2014.

]. M. Boui-14c, E. Bouillon, and . Anquetil, Stratégies de supervision pour l'apprentissage en-ligne d'un classifieur évolutif de commande gestuelles, Actes du Colloque International Francophone sur l'Écrit et le Document (CIFED), pp.293-308, 2014.

]. M. Boui-14d, E. Bouillon, and . Anquetil, Supervision Strategies for the Online Learning of an Evolving Classifier for Gesture Commands, Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), pp.2029-2034, 2014.

]. M. Boui-14e, E. Bouillon, P. Anquetil, G. Li, and . Richard, User Interaction Optimization for an Evolving Classifier of Handwritten Gesture Commands, Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp.720-725, 2014.

M. Bouillon and E. Anquetil, Handwriting Analysis with Online Fuzzy Models, Proceedings of the 17th Conference of the International Graphonomics Society (IGS), pp.71-74
URL : https://hal.archives-ouvertes.fr/hal-01165765

]. P. Li, M. Bouillon, E. Anquetil, G. Richard-abe, N. Mamitsuka et al., User and System Cross- Learning of Gesture Commands on Pen-Based Devices Références de l'état de l'art [Abe and Mamitsuka Query learning strategies using boosting and bagging, Proceeding of the 14th International Conference on Human-Computer Interaction (INTERACT) Machine Learning : Proceedings of the Fifteenth International Conference (ICML'98), pp.337-355, 1998.

. Aggarwal, On demand classification of data streams, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.503-508, 2004.
DOI : 10.1145/1014052.1014110

. Aggarwal, A framework for on-demand classification of evolving data streams. Knowledge and Data Engineering, IEEE Transactions on, vol.18, issue.5, pp.577-589, 2006.

. Aha, Instance-based learning algorithms, Machine Learning, vol.57, issue.1, pp.37-66, 1991.
DOI : 10.1145/1968.1972

URL : https://link.springer.com/content/pdf/10.1007%2FBF00153759.pdf

. Ahn, Online Character Recognition Using Elastic Curvature Matching, 2009 Seventh International Conference on Advances in Pattern Recognition, pp.395-397, 2009.
DOI : 10.1109/ICAPR.2009.94

. Almaksour, A. Almaksour, and E. Anquetil, Improving premise structure in evolving Takagi???Sugeno neuro-fuzzy classifiers, Evolving Systems, vol.2061, issue.1, pp.25-33, 2011.
DOI : 10.1117/12.165030

URL : https://hal.archives-ouvertes.fr/hal-00763296

A. Almaksour, A. Almaksour, and E. Anquetil, ILClass: Error-driven antecedent learning for evolving Takagi-Sugeno classification systems, Applied Soft Computing, vol.19, 2013.
DOI : 10.1016/j.asoc.2013.10.007

URL : https://hal.archives-ouvertes.fr/hal-00881779

. Almaksour, Synthetic handwritten gesture generation using sigma-lognormal model for evolving handwriting classifiers, 15th Biennial Conference of the International Graphonomics Society, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00741573

F. Angelov, 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.484-498, 2004.
DOI : 10.1109/TSMCB.2003.817053

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

. Breiman, Classification and regression trees, 1984.

. Brighton, H. Mellish-]-brighton, and C. Mellish, Advances in instance selection for instance-based learning algorithms, Data Mining and Knowledge Discovery, vol.6, issue.2, pp.153-172, 2002.
DOI : 10.1023/A:1014043630878

L. Cao and H. Schwartz, A directional forgetting algorithm based on the decomposition of the information matrix, Automatica, vol.36, issue.11, pp.361725-1731, 2000.
DOI : 10.1016/S0005-1098(00)00093-5

. Carlson, Coupled semi-supervised learning for information extraction, Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pp.101-110, 2010.
DOI : 10.1145/1718487.1718501

URL : http://www.cs.cmu.edu/~acarlson/papers/rtw-wsdm10.pdf

. Carpenter, . Grossberg, G. Carpenter, and S. Grossberg, The ART of adaptive pattern recognition by a self-organizing neural network, Computer, vol.21, issue.3, pp.77-88, 1988.
DOI : 10.1109/2.33

. Carpenter, Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Transactions on Neural Networks, vol.3, issue.5, pp.698-713, 1992.
DOI : 10.1109/72.159059

. Carpenter, ART 2-A : An adaptive resonance algorithm for rapid category learning and recognition, Neural NetworksSeattle International Joint Conference on, pp.151-156, 1991.

. Carpenter, Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks, vol.4, issue.6, pp.759-771, 1991.
DOI : 10.1016/0893-6080(91)90056-B

P. Cauwenberghs, G. Cauwenberghs, and T. Poggio, Incremental and Decremental Support Vector Machine Learning, pp.409-415, 2001.

N. Chakraborty, S. Chakraborty, and N. K. Nagwani, Analysis and Study of Incremental K-Means Clustering Algorithm, High Performance Architecture and Grid Computing, pp.338-341, 2011.
DOI : 10.1109/TPAMI.2007.53

J. Cheng, W. Cheng, and C. Juang, An incremental support vector machine-trained TS-type fuzzy system for online classification problems. Fuzzy Sets and Systems, pp.24-44, 2011.
DOI : 10.1016/j.fss.2010.08.006

S. L. Chiu, Fuzzy model identification based on cluster estimation, Journal of intelligent and Fuzzy systems, vol.2, issue.3, pp.267-278, 1994.

. Choo, MOSAIC: A Proximity Graph Approach for Agglomerative Clustering, Data Warehousing and Knowledge Discovery, pp.231-240, 2007.
DOI : 10.1007/978-3-540-74553-2_21

C. Chow, On optimum recognition error and reject tradeoff, IEEE Transactions on Information Theory, vol.16, issue.1, pp.41-46, 1970.
DOI : 10.1109/TIT.1970.1054406

. Choy, Neural Networks for Continuous Online Learning and Control, IEEE Transactions on Neural Networks, vol.17, issue.6, pp.1511-1531, 2006.
DOI : 10.1109/TNN.2006.881710

F. Chu and C. Zaniolo, Fast and Light Boosting for Adaptive Mining of Data Streams, Advances in Knowledge Discovery and Data Mining, pp.282-292, 2004.
DOI : 10.1007/978-3-540-24775-3_36

D. A. Cohn, Neural Network Exploration Using Optimal Experiment Design, Neural Networks, vol.9, issue.6, 1994.
DOI : 10.1016/0893-6080(95)00137-9

A. Cornuéjols and L. Miclet, Apprentissage artificiel , concepts et algorithms, Eyrolles, 2010.

V. Cortes, C. Cortes, and V. Vapnik, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

. Cover, . Hart, T. Cover, and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, vol.13, issue.1, pp.21-27, 1967.
DOI : 10.1109/TIT.1967.1053964

URL : http://ssg.mit.edu/cal/abs/2000_spring/np_dens/classification/cover67.pdf

D. R. Cox, The regression analysis of binary sequences, Journal of the Royal Statistical Society. Series B (Methodological), pp.215-242, 1958.

S. L. Crawford, Extensions to the CART algorithm, International Journal of Man-Machine Studies, vol.31, issue.2, pp.31197-217, 1989.
DOI : 10.1016/0020-7373(89)90027-8

E. Dagan, I. Dagan, and S. P. Engelson, Committee-Based Sampling For Training Probabilistic Classifiers, Proceedings of the Twelfth International Conference on Machine Learning, pp.150-157, 1995.
DOI : 10.1016/B978-1-55860-377-6.50027-X

URL : http://www.cs.biu.ac.il/~argamon/Access/ml95-sampling.ps.Z

S. Deckert, M. Deckert, and J. Stefanowski, RILL: Algorithm for Learning Rules from Streaming Data with Concept Drift, Foundations of Intelligent Systems, pp.20-29, 2014.
DOI : 10.1007/978-3-319-08326-1_3

. Del-campo-avila, Improving prediction accuracy of an incremental algorithm driven by error margins Knowledge Discovery from Data Streams, 2006.

A. Delaye and E. Anquetil, HBF49 feature set: A first unified baseline for online symbol recognition, Pattern Recognition, vol.46, issue.1, pp.117-130, 2013.
DOI : 10.1016/j.patcog.2012.07.015

URL : https://hal.archives-ouvertes.fr/hal-00933509

D. Deng and N. Kasabov, On-line pattern analysis by evolving self-organizing maps, Neurocomputing, vol.51, pp.87-103, 2003.
DOI : 10.1016/S0925-2312(02)00599-4

URL : http://divcom.otago.ac.nz/infosci/kel/CBIIS/pubs/ps-gz/ddeng_esom2.ps.gz

. Domeniconi, . Gunopulos, C. Domeniconi, and D. Gunopulos, Incremental support vector machine construction, Proceedings 2001 IEEE International Conference on Data Mining, pp.589-592, 2001.
DOI : 10.1109/ICDM.2001.989572

URL : http://www.cs.ucr.edu/~carlotta/two.ps

H. Domingos, P. Domingos, and G. Hulten, Mining high-speed data streams, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.71-80, 2000.
DOI : 10.1145/347090.347107

URL : http://magna.cs.ucla.edu/~hxwang/stream/domingos-kdd00.pdf

H. Domingos, P. Domingos, and G. Hulten, Catching up with the Data : Research Issues in Mining Data Streams, DMKD, 2001.

P. Domingos, P. Domingos, and M. Pazzani, On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning, vol.29, issue.2/3, pp.103-130, 1997.
DOI : 10.1023/A:1007413511361

. Dong, Fast SVM training algorithm with decomposition on very large data sets. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.27, issue.4, pp.603-618, 2005.

. Dubuisson, B. Masson-]-dubuisson, and M. Masson, A statistical decision rule with incomplete knowledge about classes, Pattern Recognition, vol.26, issue.1, pp.155-165, 1993.
DOI : 10.1016/0031-3203(93)90097-G

P. Elwell, R. Elwell, and R. Polikar, Incremental Learning of Concept Drift in Nonstationary Environments, IEEE Transactions on Neural Networks, vol.22, issue.10, pp.1517-1531, 2011.
DOI : 10.1109/TNN.2011.2160459

. Fan, LIBLINEAR : A Library for Large Linear Classification, J. Mach. Learn. Res, vol.9, pp.1871-1874, 2008.

. Fayyad, Advances in Knowledge Discovery and Data Mining, 1996.

. Feng, Online learning with selforganizing maps for anomaly detection in crowd scenes, Pattern Recognition (ICPR) 20th International Conference on, pp.3599-3602, 2010.

A. Fern and R. Givan, Online ensemble learning : An empirical study, Machine Learning, pp.71-109, 2003.

. Ferrer-troyano, Data streams classification by incremental rule learning with parameterized generalization, Proceedings of the 2006 ACM symposium on Applied computing , SAC '06, pp.657-661, 2006.
DOI : 10.1145/1141277.1141428

URL : https://idus.us.es/xmlui/bitstream/11441/39729/1/Data%20streams.pdf

. Fortescue, Implementation of self-tuning regulators with variable forgetting factors, Automatica, vol.17, issue.6, pp.17831-835, 1981.
DOI : 10.1016/0005-1098(81)90070-4

A. Frank and A. Asuncion, UCI machine learning repository, 2010.

. Globerson, A. Globerson, and S. T. Roweis, Metric learning by collapsing classes, Advances in neural information processing systems, pp.451-458, 2005.

. Golubitsky, . Watt, O. Golubitsky, and S. M. Watt, Distance-based classification of handwritten symbols, International Journal on Document Analysis and Recognition (IJDAR), vol.26, issue.7, pp.133-146, 2010.
DOI : 10.1002/j.1538-7305.1981.tb00272.x

N. Gorski, Optimizing error-reject trade off in recognition systems, Proceedings of the Fourth International Conference on Document Analysis and Recognition, pp.1092-1096, 1997.
DOI : 10.1109/ICDAR.1997.620677

. Grira, Active semisupervised fuzzy clustering for image database categorization, Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pp.9-16, 2005.
DOI : 10.1145/1101826.1101831

. Guo, . Greiner, Y. Guo, and R. Greiner, Optimistic Active-Learning Using Mutual Information, IJCAI, pp.823-829, 2007.

Y. Hand, D. J. Hand, and K. Yu, Idiot's Bayes?Not So Stupid After All ?, International Statistical Review, vol.69, issue.3, pp.385-398, 2001.
DOI : 10.1111/j.1751-5823.2001.tb00465.x

. Hansen, The error-reject tradeoff, Open Systems & Information Dynamics, vol.4, issue.2, pp.159-184, 1997.
DOI : 10.1023/A:1009643503022

S. Hashemi and Y. Yang, Flexible decision tree for data stream classification in the presence of concept change, noise and missing values, Data Mining and Knowledge Discovery, vol.9, issue.3, pp.95-131, 2009.
DOI : 10.1007/978-94-010-0646-0

S. O. Haykin, Adaptive Filter Theory, 2001.

. Ho, An evolving Mamdani- Takagi-Sugeno based neural-fuzzy inference system with improved interpretabilityaccuracy, 2010 IEEE International Conference on Fuzzy Systems (FUZZ), pp.1-8, 2010.
DOI : 10.1109/fuzzy.2010.5584831

. Hulten, Mining timechanging data streams, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp.97-106, 2001.

R. Hyafil, L. Hyafil, and R. L. Rivest, Constructing optimal binary decision trees is NP-complete, Information Processing Letters, vol.5, issue.1, pp.15-17, 1976.
DOI : 10.1016/0020-0190(76)90095-8

T. Hägglund, New estimation techniques for adaptive control. Institutionen för reglerteknik, 1983.

T. Hägglund, Recursive Estimation of Slowly Time Varying Parameters, pp.1137-1142, 1985.

. Iglesias, Ensemble method based on individual evolving classifiers, 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp.56-61, 2013.
DOI : 10.1109/EAIS.2013.6604105

URL : https://e-archivo.uc3m.es/bitstream/10016/23613/1/ensemble_EAIS_2013_ps.pdf

K. Jackowski, Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers, Pattern Analysis and Applications, vol.10, issue.7, pp.709-724, 2013.
DOI : 10.1162/089976698300017197

S. Jang, J. Jang, and C. Sun, Functional equivalence between radial basis function networks and fuzzy inference systems, IEEE Transactions on Neural Networks, vol.4, issue.1, pp.156-159, 1993.
DOI : 10.1109/72.182710

J. , L. , G. H. Langley, and P. , Estimating Continuous Distributions in Bayesian Classifiers, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, UAI'95, pp.338-345, 1995.

. Juang, A Self-Organizing TS-Type Fuzzy Network With Support Vector Learning and its Application to Classification Problems, IEEE Transactions on Fuzzy Systems, vol.15, issue.5, pp.998-1008, 2007.
DOI : 10.1109/TFUZZ.2007.894980

. Kaber, . Endsley, D. B. Kaber, and M. R. Endsley, Out-of-the-loop performance problems and the use of intermediate levels of automation for improved control system functioning and safety, Process Safety Progress, vol.16, issue.3, pp.126-131, 1997.
DOI : 10.1002/prs.680160304

R. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

. Karypis, Chameleon: hierarchical clustering using dynamic modeling, Computer, vol.32, issue.8, pp.3268-75, 1999.
DOI : 10.1109/2.781637

N. K. Kasabov, Evolving fuzzy neural networks : theory and applications for on-line adaptive prediction, decision making and control, Aust. J. Intell. Inf. Process. Syst.(Australia), vol.5, issue.3, pp.154-160, 1998.

. Kasabov, . Song, N. K. Kasabov, and Q. Song, DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction, IEEE Transactions on Fuzzy Systems, vol.10, issue.2, pp.144-154, 2002.
DOI : 10.1109/91.995117

URL : http://aut.researchgateway.ac.nz/bitstream/10292/616/1/S_1063-6706%2802%2902965-X.pdf

R. Klinkenberg and T. Joachims, Detecting Concept Drift with Support Vector Machines, ICML, pp.487-494, 2000.

T. Kohonen, The self-organizing map, Proceedings of the IEEE, pp.1464-1480, 1990.

. Kolter, . Maloof, J. Z. Kolter, and M. Maloof, Dynamic weighted majority: a new ensemble method for tracking concept drift, Third IEEE International Conference on Data Mining, pp.123-130, 2003.
DOI : 10.1109/ICDM.2003.1250911

URL : http://daruma.georgetown.edu/techreports/techrep_data/cstr-20030610-3.pdf

. Kosina, . Gama, P. Kosina, and J. Gama, Handling Time Changing Data with Adaptive Very Fast Decision Rules, Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases, pp.827-842, 2012.
DOI : 10.1007/978-3-642-33460-3_58

B. Kosko, Fuzzy systems as universal approximators. Computers, IEEE Transactions on, vol.43, issue.11, pp.1329-1333, 1994.
DOI : 10.1109/12.324566

R. Kulhavy, Restricted exponential forgetting in real-time identification, Automatica, vol.23, issue.5, pp.589-600, 1987.
DOI : 10.1016/0005-1098(87)90054-9

. Kulhavy, . Zarrop, R. Kulhavy, and M. B. Zarrop, On a general concept of forgetting, International Journal of Control, vol.20, issue.4, pp.905-924, 1993.
DOI : 10.1515/9781400873173

L. I. Kuncheva, Classifier Ensembles for Changing Environments, Proceedings of the 5th International Workshop on Multiple Classifier Systems, pp.1-15, 2004.
DOI : 10.1007/978-3-540-25966-4_1

. Langley, An analysis of Bayesian classifiers, AAAI, pp.223-228, 1992.

. Laskov, Incremental Support Vector Learning : Analysis, Implementation and Applications, Journal of Machine Learning Research, vol.7, pp.1909-1936, 2006.

Z. Laviola, J. J. Laviola, and R. C. Zeleznik, A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer. Pattern Analysis and Machine Intelligence, IEEE Transaction on, issue.11, pp.291917-1926, 2007.

C. Zaniolo, An adaptive nearest neighbor classification algorithm for data streams, Knowledge Discovery in Databases : PKDD 2005, pp.108-120, 2005.

. Lethelier, An automatic reading system for handwritten numeral amounts on French checks, Proceedings of 3rd International Conference on Document Analysis and Recognition, pp.92-97, 1995.
DOI : 10.1109/ICDAR.1995.598951

. Li, Semicustomizable Gestural Commands Approach and Its Evaluation, 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp.473-478, 2012.
DOI : 10.1109/icfhr.2012.267

URL : https://hal.archives-ouvertes.fr/hal-00708750

A. Liavas and P. Regalia, On the numerical stability and accuracy of the conventional recursive least squares algorithm, Liavas and Regalia, pp.88-96, 1999.
DOI : 10.1109/78.738242

. Lu, Incremental Discretization for Na??ve-Bayes Classifier, Advanced Data Mining and Applications, pp.223-238, 2006.
DOI : 10.1007/11811305_25

URL : http://www.csse.monash.edu.au/~yyang/ID06.pdf

. Lughofer, . Angelov, E. Lughofer, and P. Angelov, Handling drifts and shifts in on-line data streams with evolving fuzzy systems, Applied Soft Computing, vol.11, issue.2, pp.2057-2068, 2011.
DOI : 10.1016/j.asoc.2010.07.003

. Lughofer, . Buchtala, E. Lughofer, and O. Buchtala, Reliable All-Pairs Evolving Fuzzy Classifiers, IEEE Transactions on Fuzzy Systems, vol.21, issue.4, pp.625-641, 2013.
DOI : 10.1109/TFUZZ.2012.2226892

E. D. Lughofer, FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi???Sugeno Fuzzy Models, IEEE Transactions on Fuzzy Systems, vol.16, issue.6, pp.1393-1410, 2008.
DOI : 10.1109/TFUZZ.2008.925908

. Lühr, . Lazarescu, S. Lühr, and M. Lazarescu, Connectivity Based Stream Clustering Using Localised Density Exemplars, Advances in Knowledge Discovery and Data Mining, pp.662-672, 2008.
DOI : 10.1007/978-3-540-68125-0_62

M. Maloof, M. A. Maloof, and R. S. Michalski, Selecting examples for partial memory learning, Machine Learning, pp.27-52, 2000.

E. Mamdani, Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis, IEEE Transactions on Computers, vol.26, issue.12, pp.261182-1191, 1977.
DOI : 10.1109/TC.1977.1674779

S. Markou, M. Markou, and S. Singh, Novelty detection: a review???part 1: statistical approaches, Signal Processing, vol.83, issue.12, pp.2481-2497, 2003.
DOI : 10.1016/j.sigpro.2003.07.018

URL : http://www.dcs.ex.ac.uk/research/pann/pdf/pann_SS_086.PDF

S. Markou, M. Markou, and S. Singh, Novelty detection: a review???part 2:, Signal Processing, vol.83, issue.12, pp.2499-2521, 2003.
DOI : 10.1016/j.sigpro.2003.07.019

N. Mccallum, A. K. Mccallum, and K. Nigamy, Employing EM and pool-based active learning for text classification, Proc. International Conference on Machine Learning (ICML), pp.359-367, 1998.

. Meinhold, . Singpurwalla, R. J. Meinhold, and N. D. Singpurwalla, Understanding the Kalman filter, The American Statistician, vol.37, issue.2, pp.123-127, 1983.

. Minku, The impact of diversity on online ensemble learning in the presence of concept drift. Knowledge and Data Engineering, IEEE Transactions on, vol.22, issue.5, pp.730-742, 2010.

. Mitoma, Online character recognition based on elastic matching and quadratic discrimination, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), pp.36-40, 2005.
DOI : 10.1109/ICDAR.2005.178

URL : http://human.is.kyushu-u.ac.jp/~uchida/Papers/mitoma-icdar2005.pdf

. Morasso, Recognition experiments of cursive dynamic handwriting with self-organizing networks, Pattern Recognition, vol.26, issue.3, pp.451-460, 1993.
DOI : 10.1016/0031-3203(93)90172-S

. Moreno-seco, Extending LAESA Fast Nearest Neighbour Algorithm to Find the k Nearest Neighbours, Structural, Syntactic, and Statistical Pattern Recognition, pp.718-724, 2002.
DOI : 10.1007/3-540-70659-3_75

. Moskovitch, Improving the Detection of Unknown Computer Worms Activity Using Active Learning, KI 2007 : Advances in Artificial Intelligence, pp.489-493, 2007.
DOI : 10.1007/978-3-540-74565-5_47

A. Mouchere, H. Mouchere, and E. Anquetil, Generalization capacity of handwritten outlier symbols rejection with neural network, Tenth International Workshop on Frontiers in Handwriting Recognition, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00104310

A. Mouchere, H. Mouchere, and E. Anquetil, A Unified Strategy to Deal with Different Natures of Reject, 18th International Conference on Pattern Recognition (ICPR'06), pp.792-795, 2006.
DOI : 10.1109/ICPR.2006.193

URL : https://hal.archives-ouvertes.fr/hal-00300186

. Mouchère, Synthetic On-line Handwriting Generation by Distortions and Analogy, pp.10-13, 2007.

. Mouchère, WRITER STYLE ADAPTATION IN ONLINE HANDWRITING RECOGNIZERS BY A FUZZY MECHANISM APPROACH: THE ADAPT METHOD, International Journal of Pattern Recognition and Artificial Intelligence, vol.15, issue.01, pp.99-116, 2007.
DOI : 10.1109/91.940974

G. Moustakides, Study of the transient phase of the forgetting factor RLS, IEEE Transactions on Signal Processing, vol.45, issue.10, pp.452468-2476, 1997.
DOI : 10.1109/78.640712

. Muhlbaier, Learn$^{++}$.NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes, IEEE Transactions on Neural Networks, vol.20, issue.1, pp.152-168, 2009.
DOI : 10.1109/TNN.2008.2008326

. Muslea, Selective sampling with redundant views, AAAI/IAAI, pp.621-626, 2000.

J. Ng, A. Y. Ng, and M. I. Jordan, On Discriminative vs Generative Classifiers : A comparison of logistic regression and naive Bayes, Advances in Neural Information Processing Systems 14, pp.841-848, 2002.

. Nguyen, A survey on data stream clustering and classification, Knowledge and Information Systems, vol.15, issue.2, pp.1-35, 2015.
DOI : 10.1007/s10115-007-0070-x

. Nissim, Detecting unknown computer worm activity via support vector machines and active learning, Pattern Analysis and Applications, vol.25, issue.2, pp.459-475, 2012.
DOI : 10.1007/978-3-642-01718-6_6

. Nissim, Novel active learning methods for enhanced PC malware detection in windows OS, Expert Systems with Applications, vol.41, issue.13, pp.415843-5857, 2014.
DOI : 10.1016/j.eswa.2014.02.053

. Osuna, An improved training algorithm for support vector machines, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, pp.276-285, 1997.
DOI : 10.1109/NNSP.1997.622408

N. C. Oza, Online Bagging and Boosting, 2005 IEEE International Conference on Systems, Man and Cybernetics, pp.2340-2345, 2005.
DOI : 10.1109/ICSMC.2005.1571498

URL : http://www.cs.berkeley.edu/~oza/papers/aistats01.ps

. Parkum, Recursive forgetting algorithms, International Journal of Control, vol.3, issue.1, pp.109-128, 1992.
DOI : 10.1080/00207178808906026

W. Pedrycz, Conditional fuzzy clustering in the design of radial basis function neural networks, IEEE Transactions on Neural Networks, vol.9, issue.4, pp.601-612, 1998.
DOI : 10.1109/72.701174

. Pitrelli, Confidence modeling for handwriting recognition: algorithms and applications, International Journal of Document Analysis and Recognition (IJDAR), vol.38, issue.11, pp.35-46, 2006.
DOI : 10.1007/s10032-005-0011-8

J. R. Quinlan and . Rai, C4.5 : programs for machine learning Streamed Learning : One-Pass SVMs, 1993.

. Reznáková, SO-ARTIST, Appl. Soft Comput, issue.C, pp.31132-152, 2015.

M. Robnik-?ikonja and I. Kononenko, Theoretical and empirical analysis of ReliefF and RReliefF, Machine Learning, vol.53, issue.1/2, pp.23-69, 2003.
DOI : 10.1023/A:1025667309714

. Romeu, Automatic Adjacency Grammar Generation from User Drawn Sketches, 18th International Conference on Pattern Recognition (ICPR'06), pp.1026-1029, 2006.
DOI : 10.1109/ICPR.2006.293

URL : https://hal.archives-ouvertes.fr/inria-00103372

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, p.65386, 1958.
DOI : 10.1037/h0042519

R. , M. Roy, N. Mccallum, and A. , Toward optimal active learning through monte carlo estimation of error reduction, ICML, pp.441-448, 2001.

S. Ruping, Incremental Learning with Support Vector Machines. Data Mining, IEEE International Conference on, p.641, 2001.

D. Saad, On-line learning in neural networks, 1998.
DOI : 10.1017/CBO9780511569920

C. Sakoe, H. Sakoe, and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition. Acoustics, Speech and Signal Processing, IEEE Transactions on, vol.26, issue.1, pp.43-49, 1978.

. Salgado, Modified least squares algorithm incorporating exponential resetting and forgetting, International Journal of Control, vol.47, issue.2, pp.47477-491, 1988.
DOI : 10.1080/00207178808906026

. Salperwyck, Incremental Weighted Naive Bays Classifiers for Data Stream, Data Science, Learning by Latent Structures, and Knowledge Discovery, pp.179-190, 2015.
DOI : 10.1007/978-3-662-44983-7_16

S. Salzberg, A nearest hyperrectangle learning method, Machine Learning, vol.27, issue.3, pp.251-276, 1991.
DOI : 10.1016/B978-0-934613-41-5.50006-4

URL : https://link.springer.com/content/pdf/10.1007%2FBF00114779.pdf

. Sankaranarayanan, A fast all nearest neighbor algorithm for applications involving large pointclouds, Computers & Graphics, issue.2, pp.31157-174, 2007.
DOI : 10.1016/j.cag.2006.11.011

. Sebe, Emotion recognition using a Cauchy Naive Bayes classifier, Object recognition supported by user interaction for service robots, pp.17-20, 2002.
DOI : 10.1109/ICPR.2002.1044578

URL : http://www.ifp.uiuc.edu/~iracohen/publications/icpr02.pdf

. Seidl, Indexing density models for incremental learning and anytime classification on data streams, Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology, EDBT '09, pp.311-322, 2009.
DOI : 10.1145/1516360.1516397

URL : http://www.edbt.org/Proceedings/2009-StPetersburg/edbt/papers/p0311-Seidl.pdf

B. Settles, Active Learning Literature Survey, Computer Sciences, 2010.

C. Settles, B. Settles, and M. Craven, An analysis of active learning strategies for sequence labeling tasks, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.1070-1079, 2008.
DOI : 10.3115/1613715.1613855

. Settles, Multiple-instance active learning, Advances in neural information processing systems, pp.1289-1296, 2008.

. Silva, Data stream clustering, ACM Computing Surveys, vol.46, issue.1, p.13, 2013.
DOI : 10.1145/2522968.2522981

J. Steil, Backpropagation-decorrelation: online recurrent learning with O(N) complexity, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), pp.843-848, 2004.
DOI : 10.1109/IJCNN.2004.1380039

URL : http://ni.www.techfak.uni-bielefeld.de/files/Steil2004-BDO.pdf

. Stonebraker, The 8 requirements of real-time stream processing, ACM SIGMOD Record, vol.34, issue.4, pp.42-47, 2005.
DOI : 10.1145/1107499.1107504

. Street, . Kim, W. N. Street, and Y. Kim, A streaming ensemble algorithm (SEA) for large-scale classification, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '01, pp.377-382, 2001.
DOI : 10.1145/502512.502568

URL : http://magna.cs.ucla.edu/~hxwang/stream/street-kdd01.pdf

. Syed, Handling concept drifts in incremental learning with support vector machines, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.317-321, 1999.
DOI : 10.1145/312129.312267

URL : http://www.comp.nus.edu.sg/~sungkk/papers/KDD99_drift_final.ps.gz

. Takagi, . Sugeno, T. Takagi, and M. Sugeno, Fuzzy Identification of Systems and Its Applications to Modeling and Control. Systems, Man, and Cybernetics, IEEE Transactions on, vol.15, issue.1, pp.116-132, 1985.

C. Tappert, Cursive Script Recognition by Elastic Matching, IBM Journal of Research and Development, vol.26, issue.6, pp.765-771, 1982.
DOI : 10.1147/rd.266.0765

D. M. Tax and R. P. Duin, Support Vector Data Description, Machine Learning, vol.54, issue.1, pp.45-66, 2004.
DOI : 10.1023/B:MACH.0000008084.60811.49

URL : http://ict.ewi.tudelft.nl/~davidt/papers/ML_SVDD_04.pdf

K. Tay, Y. H. Tay, and M. Khalid, Comparison of Fuzzy ARTMAP and MLP Neural Networks for Hand-Written Character Recognition, Proceedings of the IFAC Symposium on AI in Real-Time Control, pp.363-371, 1997.
DOI : 10.1016/S1474-6670(17)41344-9

. Tencer, TITS-FM: Transductive incremental Takagi-Sugeno fuzzy models, Applied Soft Computing, vol.26, pp.531-544, 2015.
DOI : 10.1016/j.asoc.2014.09.024

K. Tomanek and U. Hahn, Semi-supervised active learning for sequence labeling, Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2, ACL-IJCNLP '09, pp.1039-1047, 2009.
DOI : 10.3115/1690219.1690291

URL : http://aclweb.org/anthology-new/P/P09/P09-1117.pdf

. Tsang, Core vector machines : Fast SVM training on very large data sets, Journal of Machine Learning Research, pp.363-392, 2005.

W. L. Tung and C. Quek, A mamdani-takagi-sugeno based linguistic neural-fuzzy inference system for improved interpretability-accuracy representation, 2009 IEEE International Conference on Fuzzy Systems, pp.367-372, 2009.
DOI : 10.1109/FUZZY.2009.5277194

. Tur, Combining active and semi-supervised learning for spoken language understanding, Speech Communication, vol.45, issue.2, pp.45171-186, 2005.
DOI : 10.1016/j.specom.2004.08.002

URL : http://www-connex.lip6.fr/~amini/RelatedWorks/TurTurSpringer_ComActiveSemiSup_SpeechCom2005.pdf

S. Uchida, S. Uchida, and H. Sakoe, A Survey of Elastic Matching Techniques for Handwritten Character Recognition, IEICE Transactions on Information and Systems, vol.88, issue.8, pp.1781-1790, 2005.
DOI : 10.1093/ietisy/e88-d.8.1781

. Usunier, Guarantees for approximate incremental SVMs, International Conference on Artificial Intelligence and Statistics, pp.884-891, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00750932

. Utgoff, Decision tree induction based on efficient tree restructuring, Machine Learning, vol.29, issue.1, pp.5-44, 1997.
DOI : 10.1023/A:1007413323501

V. N. Vapnik, The Nature of Statistical Learning Theory, 1995.

E. Vidal, New formulation and improvements of the nearest-neighbour approximating and eliminating search algorithm (AESA), Pattern Recognition Letters, vol.15, issue.1, pp.1-7, 1994.
DOI : 10.1016/0167-8655(94)90094-9

. Vuori, Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters, International Journal on Document Analysis and Recognition, vol.3, issue.3, pp.150-159, 2001.
DOI : 10.1007/PL00013555

. Walker, . Duncan, S. H. Walker, and D. B. Duncan, Estimation of the probability of an event as a function of several independent variables, Biometrika, vol.54, issue.1-2, pp.167-179, 1967.
DOI : 10.1093/biomet/54.1-2.167

. Wang, Mining Conceptdrifting Data Streams Using Ensemble Classifiers, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, pp.226-235, 2003.
DOI : 10.1145/956750.956778

URL : http://wis.cs.ucla.edu/~hxwang/publications/tckdd.pdf

K. Q. Weinberger and L. K. Saul, Distance metric learning for large margin nearest neighbor classification, The Journal of Machine Learning Research, vol.10, pp.207-244, 2009.

G. Widmer, Combining robustness and flexibility in learning drifting concepts, ECAI, pp.468-472, 1994.

. Widmer, . Kubat, G. Widmer, and M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning, pp.69-101, 1996.
DOI : 10.1007/BF00116900

. Wilpon, Automatic recognition of keywords in unconstrained speech using hidden Markov models, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.38, issue.11, pp.381870-1878, 1990.
DOI : 10.1109/29.103088

. Wobbrock, Userdefined gestures for surface computing, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '09, pp.1083-1092, 2009.
DOI : 10.1145/1518701.1518866

URL : http://research.microsoft.com/~merrie/papers/SurfaceGestures_CHI2009.pdf

. Wobbrock, Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes, Proceedings of the 20th annual ACM symposium on User interface software and technology , UIST '07, pp.159-168, 2007.
DOI : 10.1145/1294211.1294238

URL : http://www.research.microsoft.com/~awilson/papers/uist-07.1.pdf

Y. , W. Webb, and G. I. , A comparative study of discretization methods for naive-bayes classifiers, Proceedings of PKAW, 2002.

. Yuan, Recent Advances of Large-Scale Linear Classification, Proceedings of the IEEE, pp.2584-2603, 2012.
DOI : 10.1109/JPROC.2012.2188013

L. A. Zadeh, Fuzzy sets, Information and Control, vol.8, issue.3, pp.338-353, 1965.
DOI : 10.1016/S0019-9958(65)90241-X

G. Zhang, Neural networks for classification: a survey, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.30, issue.4, pp.451-462, 2000.
DOI : 10.1109/5326.897072

URL : https://cours.etsmtl.ca/sys843/pdf/PZhang2000.pdf

. Zhaoxin, Recognize multi-touch gestures by graph modeling and matching. Drawing, Handwriting Processing Analysis : New Advances and Challenges, p.51

X. Zhu, Semi-Supervised Learning Literature Survey, 2005.

. Zhu, Combining active learning and semi-supervised learning using gaussian fields and harmonic functions, ICML workshop on the continuum from labeled to unlabeled data in machine learning and data mining, pp.58-65, 2003.

I. ?liobaite, Learning under concept drift : an overview. arXiv preprint arXiv, pp.1010-4784, 2010.

.. Apprentissage-en-ligne, 28 1.6.2.1 Apprentissage incrémental sur flux, p.31

.. Capacité-d-'auto-Évaluation-et-de-rejet-des-données, 98 5.5.1 Option de rejet de distance, p.100

.. Stratégie-de-supervision-active-en-ligne, 109 6.2.1 Supervision implicite : sans interaction avec l'utilisateur

D. Bases-de, 127 7.2.2.1 ILGDB : Intuidoc Loustic Gesture Data Base, p.131

.. Adaptation-aux-changements-de-concepts, 138 7.5.1 Changements de concepts progressifs, p.141

.. Apprentissage-hors-ligne-et-apprentissage-en-ligne, 29 1.11 Types de supervision de l'apprentissage d'un classifieur, p.33

.. Inertie-du-système-lors-de-l-'ajout-de-classes, 138 7.10 Adaptation aux changements de concepts légers, p.140

.. Différence-de-taux-d-'apprentissage-croisé, 144 7.15 Option de rejet de confusion multi-seuils, p.147