A. Martin, Comparative study of information fusion methods for sonar images classification, 2005 7th International Conference on Information Fusion, pp.25-29, 2005.
DOI : 10.1109/ICIF.2005.1592022
URL : https://hal.archives-ouvertes.fr/hal-00518722

A. Martin, Fusion for Evaluation of Image Classification in Uncertain Environments, The Eighth International Conference on Information Fusion, pp.10-13, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00281900

H. Laanaya, A. Martin, D. Aboutajdine, and A. Khenchaf, Knowledge Discovery on Database for Seabed Characterization, Magrebian Conference on Information Technologies, pp.7-9, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00518663

J. Weston and C. Watkins, Support Vector Machines for Multi-Class Pattern Recognition Machines, 1998.

A. Martin and C. Osswald, Toward a combination rule to deal with partial conflict and specificity in belief functions theory, 2007 10th International Conference on Information Fusion, 2007.
DOI : 10.1109/ICIF.2007.4408007
URL : https://hal.archives-ouvertes.fr/hal-00281897

A. Aregui and T. Denoeux, Fusion of one-class classifier in the belief function framework, International Conference on Information Fusion, 2007.

B. Quost, T. Denoeux, and M. Masson, Pairwise classifier combination using belief functions, Pattern Recognition Letters, vol.28, issue.5, pp.644-653, 2007.
DOI : 10.1016/j.patrec.2006.11.002
URL : https://hal.archives-ouvertes.fr/hal-00445480

J. C. Platt, Probabilities for SV Machines, Advances in Large Margin Classifiers, pp.61-74, 1999.

C. Frélicot, On unifing probabilistic/fuzzy and possibilistic rejectionbased classifier from possibilistic clustered noisy data, Advance in Pattern Recognition, series Lecture Notes in Computer Science, pp.736-745, 1998.

J. Liu and P. Gader, Neural networks with enhanced outlier rejection ability for off-line handwritten word recognition, Pattern Recognition, pp.2061-2071, 2002.
DOI : 10.1016/S0031-3203(01)00191-1

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

J. Milgram, M. Cherier, and R. Sabourin, One-Against-One or One-Against-All: Which One is Better for Hanwriting Recognition with SVMs?, 10th International Workshop on Frontiers in Handwriting Recognition, 2006.

Y. Liu, Y. F. Zhen, and G. Bakiri, One-Against-All Multi-Class SVM Classification Using Reliability Measures Solving Multiclass Learning Problems via Error-Correcting Output Codes, IEEE International Joint Conference on Neural Networks Dietterich, pp.849-854, 1995.

A. Lauberts and D. Lindgren, Generalization Ability of a Support Vector Classifier Applied to Vehicle Data in a Microphone Network, 2006 9th International Conference on Information Fusion, 2006.
DOI : 10.1109/ICIF.2006.301636

J. C. Platt, N. Cristianini, and J. Shawe-taylor, Large Margin DAGs for Multiclass Classification, Advances in Neural Information Processing Systems, pp.547-553, 2000.

A. P. Dempster, Upper and Lower probabilities induced by a multivalued mapping, Annals of Mathematical Statistics, pp.325-339, 1967.

G. Shafer, A mathematical theory of evidence, 1976.

. Ph and . Smets, Constructing the pignistic probability function in a context of uncertainty, Uncertainty in Artificial Intelligence, vol.5, pp.29-39, 1990.

. Ph and . Smets, The Combination of Evidence in the Transferable Belief Model, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.447-458, 1990.

S. , L. Hégarat-mascle, I. Bloch, and D. Vidal-madjar, Application of Dempster-Shafer Evidence Theory to Unsupervised Classification in Multisource Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, vol.35, issue.4, pp.1018-1031, 1997.

A. Appriou, Approche générique de la gestion de l'incertain dans les processus de fusion multisenseur, Traitement du Signal, pp.307-319, 2005.

H. Laanaya, A. Martin, D. Aboutajdine, and A. Khenchaf, Seabed classification using belief multi-class support vector machines, Marine Environment Characterization, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00518694

H. T. Lin, C. Lin, and C. Weng, A note on Platt???s probabilistic outputs for support vector machines, Machine Learning, 2008.
DOI : 10.1007/s10994-007-5018-6

B. Schölkopf, J. C. Platt, J. Shawe-taylor, and A. J. Smola, Estimating the Support of a High-Dimensional Distribution, Microsoft Research, 1999.
DOI : 10.1214/aos/1069362732

Z. Elouedi, K. Mellouli, . Ph, and . Smets, Assessing Sensor Reliability for Multisensor Data Fusion Within the Transferable Belief Model, IEEE Transactions on Systems, Man, and Cybernetics -Part B: Cybernetics LIBSVM: a library for support vector machines, pp.782-787, 2001.
DOI : 10.1109/TSMCB.2003.817056